# Ridge-penalized adaptive Mantel test and its application in imaging genetics

arXiv: https://arxiv.org/abs/2103.02156 · HTML: https://mlsanity.com/paper/2103.02156/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2021.

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

- [Predictive Performance Test based on the Exhaustive Nested Cross-Validation for High-dimensional data](https://mlsanity.com/paper/2408.03138/) (2024)

## Abstract (arXiv)

We propose a ridge-penalized adaptive Mantel test (AdaMant) for evaluating the association of two high-dimensional sets of features. By introducing a ridge penalty, AdaMant tests the association across many metrics simultaneously. We demonstrate how ridge penalization bridges Euclidean and Mahalanobis distances and their corresponding linear models from the perspective of association measurement and testing. This result is not only theoretically interesting but also has important implications in penalized hypothesis testing, especially in high dimensional settings such as imaging genetics. Applying the proposed method to an imaging genetic study of visual working memory in health adults, we identified interesting associations of brain connectivity (measured by EEG coherence) with selected genetic features.