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Model organisms and human studies have led to increasing empirical evidence that interactions among genes contribute broadly to genetic variation of complex traits. In the presence of gene-by-gene interactions, the dimensionality of the feature space becomes extremely high relative to the sample size. This imposes a significant methodological challenge in identifying gene-by-gene interactions. In the present paper, through a Gaussian graphical model framework, we translate the problem of identifying gene-by-gene interactions associated with a binary trait
This is a pre-copyedited, author-produced PDF of an article accepted for publication in Biometrika following peer review. The version of record [Xia, Y., Cai, T., & Cai, T.T. Testing Differential Networks with Applications to the Detection of Gene-Gene Interactions. Biometrika 102, no. 2: pp. 247-266] is available online at: http://dx.doi.org/10.1093/biomet/asu074
differential network, false discovery rate, Gaussian graphical model, gene-by-gene interaction, highdimensional precision matrix, large scale multiple testing
Xia, Y., Cai, T., & Cai, T. (2015). Testing Differential Networks with Applications to Detection of Gene-Gene Interactions. Biometrika, 102 (2), 247-266. http://dx.doi.org/10.1093/biomet/asu074
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Date Posted: 25 October 2018
This document has been peer reviewed.