I am looking at mutations in exome data and would like to identify which genes harbour more mutations than would be expected given the average mutation rate of my cohort.
Currently, I model the number of mutations as a Poisson random variable with parameter lambda = average mutation rate per Mb * gene length in Mb. However, the expected number of mutations is very low, and the observations appear significant (p < 0.05) in all cases. E.g. I observe one mutation where I exepct 0.019948561 mutations, for a p-value = 1.963461e-04.
Is there a better way to do this? Should I improve the model, or is there a clever way to correct the p-values? In R,
p.adjust results in a very small change to the p-values.