It could be that only a few potential haplotypes (or potential other variables) really affect the outcome. If this is suspected to be the case, then Stepwise Regression can be appropriate.

Starting with either the null model or the reduced model with covariates and interaction terms only (depending on which type of regression was specified), successive models are created, each one using one more regressor than the previous model.

To pick which regressor to use for the next model, each of the unused regressors in turn is tried out by adding it to the current model. The P-value of the trial model as a “full model” vs. the current model as a “reduced model” is found, and the model with the best (smallest) P-value found this way is used. However, if no P-value is better than the “P-value cutoff” that was specified, the stepwise method stops, and declares the current model as the end result. (Of course, the stepwise method will also stop if all possible regressors have been used up.)

From the standpoint of further analysis, this end result becomes the “full model” for this set of potential regressors.