There is one saving grace for us which is that the predictor we used is significantly less powerful than ones we know to exist.
I think when you account for both the squaring issue, the indirect effect things, and the more powerful predictors, they’re going to roughly cancel out.
Granted, the more powerful predictor itself isn’t published, so we can’t rigorously evaluate it either which isn’t ideal. I think the way to deal with this is to show a few lines: one for the “current publicly available GWAS”, one showing a rough estimate of the gain using the privately developed predictor (which with enough work we could probably replicate), and then one or two more for different amounts of data.
All of this together WILL still reduce the “best case scenario” from editing relative to what we originally published (because with the better predictor we’re closer to “perfect knowledge” than where we were with the previous predictor.
At some point we’re going to re-run the calculations and publish an actual proper writeup on our methodology (likely with our code).
Also I just want to say thank you for taking the time to dive deep into this with us. One of the main reasons I post on LessWrong is because there is such high quality feedback relative to other sites.
There is one saving grace for us which is that the predictor we used is significantly less powerful than ones we know to exist.
I think when you account for both the squaring issue, the indirect effect things, and the more powerful predictors, they’re going to roughly cancel out.
Granted, the more powerful predictor itself isn’t published, so we can’t rigorously evaluate it either which isn’t ideal. I think the way to deal with this is to show a few lines: one for the “current publicly available GWAS”, one showing a rough estimate of the gain using the privately developed predictor (which with enough work we could probably replicate), and then one or two more for different amounts of data.
All of this together WILL still reduce the “best case scenario” from editing relative to what we originally published (because with the better predictor we’re closer to “perfect knowledge” than where we were with the previous predictor.
At some point we’re going to re-run the calculations and publish an actual proper writeup on our methodology (likely with our code).
Also I just want to say thank you for taking the time to dive deep into this with us. One of the main reasons I post on LessWrong is because there is such high quality feedback relative to other sites.