Genetic Interaction (GI) data provides a means for exploring the structure and function of pathways in a cell. Coherent value bicliques (submatrices) in GI data represents functionally similar gene modules or protein complexes. However, no systematic approach has been proposed for exhaustively enumerating all coherent value submatrices in such data sets, which is the problem addressed in this paper. Using a monotonic range measure to capture the coherence of values in a submatrix of an input data matrix, we propose a two-step Apriori-based algorithm for discovering all nearly constant value submatrices, referred to as Range Constrained Blocks. By systematic evaluation on an extensive genetic interaction data set, we show that the coherent value submatrices represent groups of genes that are functionally related than the submatrices with diverse values. We also show that our approach can exhaustively find all the submatrices with a range less than a given threshold, while the other competing approaches can not find all such submatrices.