how many individuals in the study could have p-Val that will survive a p-Value adjustment?

Power is one thing but the stringency of the pvalue adjustment will also depend on the distribution of p-values: if your effect is large, then you'll have many low p-val and the adjustment will not affect them much. But if the effect is subtle, then the pval distribution is more linear and the adjustment is more stringent.

In the end, I don't think one can answer that question before running the experiment.

Hopefully, someone will come up with a more useful answer :)

EDIT : my answer is valid for p-val adjustment methods that control FDR (False Discovery Rate) such as the Benjamini method. However the dependancy on the pvalue distribution doesn't apply to methods that control the ‘family-wise error rate’ such as the Bonferroni correction.

See : Noble, W. S. (2009). How does multiple testing correction work? Nature Biotechnology, 27(12), 1135–7. doi:10.1038/nbt1209-1135

I hope these posts will help you:

How To Calculate A Sample Size

Formula Of Power/Sample Size Calculation In Gwas

How To Combine P-Values Obtained With Different Approaches?

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