I'm trying to figure out if I should be filtering out GWAS hits that have high standard error and I'm not quite sure what to do. It seems like it might not matter, because the standard error is used to calculate the t-statistic, which is then used to calculate the p-value. So in a way it's already built in. But reporting SNPs that have very high standard error doesn't seem quite right. What's the best way to handle this?
I think the effect size is more of an issue than the standard error.
If the standard error suggests that the effect direction could change sign, then it might be a good idea to filter it out. Otherwise, if the effect size remains large (or at least positive) even after accounting for a large error, then [to me] that would attach more weight to the likelihood that the association is valid.
I did a very quick analysis of the results of one UK Biobank study on Twitter, where I used the statistic of "the least extreme value in a 95% confidence interval" in a Manhattan plot instead of the more commonly-used p-value: