Newer regression techniques that make use of regularization and shrinkage parameters to control for collinearity and overfitting can be used to overcome these problems. Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81 Tibshirani82 compared the relative merits of standard stepwise regression, ridge regression, and the LASSO in different non-genetic contexts and concluded that each method seems is best suited for different specific settings, depending on the number and effect sizes of the predictors. This is problematic in the context of genetic association analyses since one will not necessarily know a priori how many common, rare, or collapsed sets of variants might influence a phenotype, nor what kind of effects those variants have. One possible solution to this problem is to devise methods that combine elements of many different regression procedures, such as the ‘bridge (GPS)’ regression procedure of Friedman83 that exploits constructs forming the basis for both ridge and LASSO-based regression. Alternatively, ‘ensemble’ methods or ‘super learners’ that combine the results of
