Sparse estimation of gene-gene interactions in prediction models.
Current assessment of gene-gene interactions is typically based on separate parallel analysis, where each interaction term is tested separately, while less attention has been paid on simultaneous estimation of interaction terms in a prediction model. As the number of interaction terms grows fast, sparse estimation is desirable from statistical and interpretability reasons. There is a large literature on sparse estimation, but there is a natural hierarchy between the interaction and its corresponding main effects that requires special considerations. We describe random-effect models that impose sparse estimation of interactions under both strong and weak-hierarchy constraints. We develop an estimation procedure based on the hierarchical-likelihood argument and show that the modelling approach is equivalent to a penalty-based method, with the advantage of the models being more transparent and flexible. We compare the procedure with some standard methods in a simulation study and illustrate its application in an analysis of gene-gene interaction model to predict body-mass index.
1 Quantitative Biomedical Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA.
Sangin
S
Lee
2 Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden.
Yudi
Y
Pawitan
3 Department of Medical Sciences, Molecular Epidemiology and Science for Life Laboratory, Uppsala University, Uppsala, Sweden.
Erik
E
Ingelsson
4 Department of Statistics, Seoul National University, Seoul, Korea.
Youngjo
Y
Lee
D000465:Algorithms; D015992:Body Mass Index; D004843:Epistasis, Genetic; D020022:Genetic Predisposition to Disease / Q000235:genetics; D006801:Humans; D016013:Likelihood Functions; D015233:Models, Statistical
Group variable selection; hierarchical-likelihood; random-effect model; structured variable selection