There are many frameworks to combine the results of multiple tests for positive selection, identifying only those highlighted by more than one method. However this is a complex problem, complicated by the fact that each selection test measures a slightly different type of selection event (e.g. complete or incomplete sweeps, recent or ancient, population-specific sweeps, etc..)

The easiest way is to combine the p-values using Fisher's method. This is not completely correct because the p-values from multiple tests are not always independent, but in general it is accepted. For a discussion, see:

- Peng et al 2010 http://www.nature.com/ejhg/journal/v18/n1/full/ejhg2009115a.html

- Zaykin et al 2008 http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2569904/

- This biostar discussion How To Combine P-Values Obtained With Different Approaches?

- wikipedia article http://en.wikipedia.org/wiki/Fisher%27s_method

A more sophisticated approach is to use machine learning to combine the tests together. However, this usually requires calculating simulations using different demographic models, in order to have a training and test datasets.

- Grossman 2010, CMS http://www.ncbi.nlm.nih.gov/pubmed/20056855

- Lin et al 2011, Boosting http://www.ncbi.nlm.nih.gov/pubmed/21041556