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

Dustin Pluta, Tong Shen, Gui Xue, Chuansheng Chen, Hernando Ombao, Zhaoxia Yu

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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 healthy adults, we identified interesting associations of brain connectivity (measured by electroencephalogram coherence) with selected genetic features.
Original languageEnglish (US)
JournalStatistics in Medicine
StatePublished - Jul 2 2021

Bibliographical note

KAUST Repository Item: Exported on 2021-07-15
Acknowledgements: We thank Professor Daniel L. Gillen, University of California, Irvine for the helpful discussions. We greatly appreciate the reviewers' insightful, careful, and constructive comments on our manuscript. These valuable comments have helped us improve the quality of our work.

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability


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