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

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

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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