Deep interpretability for GWAS
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.
- Publication:
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arXiv e-prints
- Pub Date:
- July 2020
- DOI:
- 10.48550/arXiv.2007.01516
- arXiv:
- arXiv:2007.01516
- Bibcode:
- 2020arXiv200701516S
- Keywords:
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- Computer Science - Machine Learning;
- Quantitative Biology - Genomics;
- Statistics - Applications;
- Statistics - Machine Learning
- E-Print:
- Accepted at ICML 2020 workshop on ML Interpretability for Scientific Discovery