MOTIVATION: Very little attention has been given to gene selection procedures based on intergene correlation structure, which is often neglected in the context of differential gene expression analysis. We propose a statistical procedure to select genes that have different associations with others across different phenotypes. This procedure is based on a new gene association score, called the covariance distance. RESULTS: We apply the proposed method, along with two alternative methods, to several simulated datasets and find out that our method is much more powerful than the other two. For biological data, we demonstrate that the analysis of differentially associated genes complements the analysis of differentially expressed genes. Combining both procedures provides a more comprehensive functional interpretation of the experimental results. AVAILABILITY: The code is downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.