Genetically altering IQ is more or less about flipping a sufficient number of IQ-decreasing variants to their IQ-increasing counterparts. This sounds overly simplified, but it’s surprisingly accurate; most of the variance in the genome is linear in nature, by which I mean the effect of a gene doesn’t usually depend on which other genes are present.
So modeling a continuous trait like intelligence is actually extremely straightforward: you simply add the effects of the IQ-increasing alleles to to those of the IQ-decreasing alleles and then normalize the score relative to some reference group.
If the mechanism of most of these genes is that their variants push something analogous to a hyperparameter in one direction or the other, and the number of parameters is much smaller than the number of genes, then this strategy will greatly underperform the simulated prediction. This is because the cumulative effect of flipping all these genes will be to move hyperparameters towards optimal but then drastically overshoot the optimum.
If you were to flip enough variants to push someone far outside the human range then that’s almost certainly correct. But the linear model holds remarkably well within the current human range, and likely to some degree outside of it.
But I am not too concerned about this because we can do multiples rounds of fewer edits and validate between rounds.
That seems so obviously correct as a starting point, not sure why the community here doesn’t agree by default. My prior for each potential IQ increase would be that diminishing returns would kick in—I would only update against when actual data comes in disproving that.
Well, we can just see empirically that linear models predict outliers pretty well for existing traits. For example, here’s a graph the polygenic score for Shawn Bradley, a 7′6″ former NBA player. He does indeed show up as a very extreme data point on the graph:
I think your general point stands: if we pushed far enough into the tails of these predictors, the actual phenotypes would almost certainly diverge from the predicted phenotypes. But the simple linear models seem to hold quite well eithin the existing human distribution.
I think height is different to IQ in terms of effect. There are simple physical things that make you bigger, I expect height to be linear for much longer than IQ.
Then there are potential effects, like something seems linear until OOD, but such OOD samples don’t exist because they die before birth. If that was the case it would look like you could safely go OOD. Would certainly be easier if we had 1 million mice with such data to test on.
If the mechanism of most of these genes is that their variants push something analogous to a hyperparameter in one direction or the other, and the number of parameters is much smaller than the number of genes, then this strategy will greatly underperform the simulated prediction. This is because the cumulative effect of flipping all these genes will be to move hyperparameters towards optimal but then drastically overshoot the optimum.
If you were to flip enough variants to push someone far outside the human range then that’s almost certainly correct. But the linear model holds remarkably well within the current human range, and likely to some degree outside of it.
But I am not too concerned about this because we can do multiples rounds of fewer edits and validate between rounds.
That seems so obviously correct as a starting point, not sure why the community here doesn’t agree by default. My prior for each potential IQ increase would be that diminishing returns would kick in—I would only update against when actual data comes in disproving that.
Well, we can just see empirically that linear models predict outliers pretty well for existing traits. For example, here’s a graph the polygenic score for Shawn Bradley, a 7′6″ former NBA player. He does indeed show up as a very extreme data point on the graph:
I think your general point stands: if we pushed far enough into the tails of these predictors, the actual phenotypes would almost certainly diverge from the predicted phenotypes. But the simple linear models seem to hold quite well eithin the existing human distribution.
I think height is different to IQ in terms of effect. There are simple physical things that make you bigger, I expect height to be linear for much longer than IQ.
Then there are potential effects, like something seems linear until OOD, but such OOD samples don’t exist because they die before birth. If that was the case it would look like you could safely go OOD. Would certainly be easier if we had 1 million mice with such data to test on.