# A scalable hierarchical lasso for gene-environment interactions

arXiv: https://arxiv.org/abs/2103.13510 · HTML: https://mlsanity.com/paper/2103.13510/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Mar 2021.

## Built on by (most cited first, 2 of 2)

- [The Choice of Normalization Influences Shrinkage in Regularized Regression](https://mlsanity.com/paper/2501.03821/) (2025)
- [Dual Feature Reduction for the Sparse-group Lasso and its Adaptive Variant](https://mlsanity.com/paper/2405.17094/) (2024)

## Abstract (arXiv)

We describe a regularized regression model for the selection of gene-environment (GxE) interactions. The model focuses on a single environmental exposure and induces a main-effect-before-interaction hierarchical structure. We propose an efficient fitting algorithm and screening rules that can discard large numbers of irrelevant predictors with high accuracy. We present simulation results showing that the model outperforms existing joint selection methods for (GxE) interactions in terms of selection performance, scalability and speed, and provide a real data application. Our implementation is available in the gesso R package.