Structured Detection of Interactions with the Directed Lasso

Hristina Pashova (), Michael LeBlanc () and Charles Kooperberg ()
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Hristina Pashova: University of Washington
Michael LeBlanc: Fred Hutchinson Cancer Research Center
Charles Kooperberg: Fred Hutchinson Cancer Research Center

Statistics in Biosciences, 2017, vol. 9, issue 2, No 20, 676-691

Abstract: Abstract When considering low-dimensional gene–treatment or gene–environment interactions, we might suspect groups of genes to interact with treatment or environment in a similar way. For example, genes associated with related biological processes might interact with an environmental factor or a clinical treatment in its effect on a phenotype correspondingly. We use the idea of a structured interaction model together with penalized regression to limit the model complexity in a model in which we believe the interactions might behave in a similar way. We propose the directed lasso, a regression modeling strategy using a pairwise fused lasso penalty to encourage interaction model simplicity through fusion of effect size. We compare the performance of the directed lasso to the lasso and other methods in a simulation study and on data sampled from a breast cancer clinical trial.

Keywords: Gene–environment interaction; Gene–treatment interaction; Interaction; Lasso; Fusion (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s12561-016-9184-6

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