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.　©　2017　IEEE.