Another thing: if you have a test for which g explains the lion’s share of the heritable variance, but there are also other traits which contribute heritable variance, and the other traits are similarly polygenic as g (similar number of causal variants), then by picking the top-N expected effect size edits, you’ll probably mostly/entirely end up editing variants which affect g. (That said, if the other traits are significantly less polygenic than g then the opposite would happen.)
I should mention, when I wrote this I was assuming a simple model where the causal variants for g and the ‘other stuff’ are disjoint, which is probably unrealistic—there’d be some pleiotropy.
I should mention, when I wrote this I was assuming a simple model where the causal variants for g and the ‘other stuff’ are disjoint, which is probably unrealistic—there’d be some pleiotropy.