Strategies to disentangle the effect of two genes on phenotype I am analyzing the effect of one putative tumor suppressor (and its loss) on a specific cancer phenotype. This putative tumor suppressor is known in literature to have an effect on the growth of tumors in mouse models.

However, in the same region of this gene there's a second well-known tumor suppressor - actually a very powerful one. By looking at the data from cBio portal, there's a very strong co-occurrence of loss on both tumor suppressors in a pan-cancer analysis, that is they are almost always lost together.

Therefore, I would like to disentangle the effect of the two genes, and to analyze only the effect of my putative candidate on the phenotype. The simplest approach is to use only the samples that have a single loss (only the loss of my putative candidate, but not the loss of the confounding tumor suppressor). However, only ~10% of all samples shows such behavior, and this 10% is distributed across all cancer types, decreasing the statistical power of the analysis - since I would like to test each cancer type separately to avoid mixing incompatible data.

Which kind of strategy/analysis can be used in this case?
Maybe you could fit a mixed model to predict phenotype, using the two genes as fixed effects and cancer type as a random effect, and ask the model for the separate effects of each gene, and the interaction between them.