Hi @plicht - an appropriate q-value threshold is highly context-dependent and I’m not aware of whether the optimal threshold can be estimated on a per dataset basis. The usual recommendation is that you should always use a combination of effect size estimate (i.e., strong vs. weak association), data distribution, and domain knowledge to call out a 'statistically significant' association as 'biologically relevant'.
As @Kelsey_Thompson mentioned elsewhere in a different context, this can be done, for example, by checking the box or scatter plots to make sure your results appear to be true, unaffected by a few potential outliers. You can also sort your results by effect size instead of statistical significance or a combination thereof and pinpoint the most relevant results based on a meaningful effect size threshold possibly based on prior domain knowledge.
Hope this helps!