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

TL;DR

The paper introduces AdaMant, a ridge-penalized adaptive Mantel test for high-dimensional association analysis, demonstrating its theoretical properties and application in imaging genetics to identify brain-genetic links.

Contribution

It develops a novel ridge-penalized adaptive Mantel test that unifies Euclidean and Mahalanobis distances for high-dimensional data analysis.

Findings

Identified significant associations between EEG coherence and genetic features.

Bridged Euclidean and Mahalanobis distances through ridge penalization.

Provided theoretical insights into penalized hypothesis testing in high dimensions.

Abstract

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.