Is there a relationship between the p-values obtained in a GWAS and the standard error of the effect size of a SNP that can that can be explained either explicitly or intuitively? Methods for prediction based on effect sizes, such as PRSice, don't incorporate the standard error into the prediction model; is this because it is too complicated to do algorithmically or is it too difficult to define a "bad" standard error to prune out?

Are we in any way filtering for "good" SEs by filtering by P-value during PRS; is there a relationship between SE and P-value such that we might expect our top SNPs to have SEs that are not very large relative to its effect size?