Ridge-penalized adaptive Mantel test and its application in imaging genetics
KAUST DepartmentBiostatistics Group
Computer, Electrical and Mathematical Science and Engineering (CEMSE) Division
Preprint Posting Date2021-03-03
Online Publication Date2021-07-02
Print Publication Date2021-10-30
Embargo End Date2022-07-02
Permanent link to this recordhttp://hdl.handle.net/10754/668442
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AbstractWe 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.
CitationPluta, D., Shen, T., Xue, G., Chen, C., Ombao, H., & Yu, Z. (2021). Ridge-penalized adaptive Mantel test and its application in imaging genetics. Statistics in Medicine. doi:10.1002/sim.9127
SponsorsWe 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.
JournalStatistics in Medicine