Low number of significant mutations

[srithegreat]

I have a WGS cohort with 36 patients. When I run mutsig2cv with hg38 genome, I get only 5 genes with q<0.05, rest all are q-value of 1.
Is it because of sample size? Does mutsig2cv need a minimum number of samples for it to give reliable numbers or there are some parameters that could be trained to get better results

Any help appreciated.

[julianhess]

Yes, it is likely due to the size of your cohort. The "minimum cohort size" depends completely on the population frequency of drivers you want to discover, which is a function of both cohort size and background mutation rate. You need more patients to discover rare drivers, and fewer patients to identify common drivers.

See here for a discovery power calculator. For example, a 36 patient cohort with a background mutation rate of 1/Mb has power to discover 82% of genes mutated in 20% of patients, 28% power for genes in 10% of patients, 4% power for genes in 5% of patients, and essentially no power at all to discover genes mutated in ≤3% of patients.

Given this, 5 significant genes is a completely reasonable number for a cohort of 36 patients.

MutSig's statistical model is designed to be as robust as possible, and thus does not have any parameter to adjust.

[srithegreat]

Thanks for the quick response. That explains the results. What would be your suggestion/approach to do in such cases, use the results as such or use some alternative strategy. By the way my cohort is of cervical cancer.