title: Penalized logistic regression for detecting gene interactions.
abstract: We propose using a variant of logistic regression (LR) with (L)_(2)-regularization to fit gene-gene and gene-environment interaction models. Studies have shown that many common diseases are influenced by interaction of certain genes. LR models with quadratic penalization not only correctly characterizes the influential genes along with their interaction structures but also yields additional benefits in handling high-dimensional, discrete factors with a binary response. We illustrate the advantages of using an (L)_(2)-regularization scheme and compare its performance with that of "multifactor dimensionality reduction" and "FlexTree," 2 recent tools for identifying gene-gene interactions. Through simulated and real data sets, we demonstrate that our method outperforms other methods in the identification of the interaction structures as well as prediction accuracy. In addition, we validate the significance of the factors selected through bootstrap analyses.
authors.affiliation: Google Inc., 1600 Amphitheatre Parkway, Mountain View, CA 94043, USA. meeyoung@google.com
authors.forename: Mee Young
authors.initials: MY
authors.lastname: Park
authors.forename: Trevor
authors.initials: T
authors.lastname: Hastie
mesh_terms: D003198:Computer Simulation; D004843:Epistasis, Genetic; D005260:Female; D006801:Humans; D006973:Hypertension / Q000235:genetics; D016015:Logistic Models; D008957:Models, Genetic; D001749:Urinary Bladder Neoplasms / Q000235:genetics