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Ridge‐penalized adaptive Mantel test and its application in imaging genetics
Author(s) -
Pluta Dustin,
Shen Tong,
Xue Gui,
Chen Chuansheng,
Ombao Hernando,
Yu Zhaoxia
Publication year - 2021
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.9127
Subject(s) - mahalanobis distance , ridge , mantel test , computer science , association (psychology) , imaging genetics , association test , artificial intelligence , statistics , mathematics , biology , neuroimaging , genetics , psychology , genotype , genetic variation , neuroscience , single nucleotide polymorphism , paleontology , gene , psychotherapist
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.