A multi-stage approach to detect gene-gene interactions associated with multiple correlated phenotypes

Multiple correlated phenotypes often appear in complex traits or complex diseases. These correlated phenotypes are useful in identifying gene-gene interactions associated with complex traits or complex disease more effectively. Some approaches have been proposed to use correlation among multiple phenotypes to identify gene-gene interactions that are common to multiple phenotypes. However these approaches either didn't find truly gene-gene interactions or got results which are hard to explain, especially by using all correlated phenotypes to identify gene-gene interactions, they made identified interactions unreliable. In this paper, we propose Multivariate Quantitative trait based Ordinal MDR (MQOMDR) algorithm to effectively identify gene-gene interactions associated with multiple correlated phenotypes by selecting the best classifier according to not only the training accuracy of the phenotype under consideration but also other phenotypes with weights determined mainly by their pair correlation with the phenotype under consideration and also by repeated selection process to make use of truly useful correlated phenotypes. Experimental results on two real datasets show that our algorithm has better performance in identifying gene-gene interactions associated with multiple correlated phenotypes.