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Can you comment your current thoughts on rare haplotypes?

Could you expand on what sense you have 'taken this into account' in your models? What are you expecting to achieve by editing non-causal SNPs?

If we have a SNP that we're 30% sure is causal, we expect to get 30% of its effect conditional on it being causal. Modulo any weird interaction stuff from rare haplotypes, which is a potential concern with this approach.

The first paper I linked is about epistasic effects on the additivity of a QTLs for quantitative trait, specifically heading date in rice, so this is evidence for this sort of effect on such a trait.

I didn't read your first comment carefully enough; I'll take a look at this.

I definitely don't expect additivity holds out to like +20 SDs. We'd be aiming for more like +7 SDs.

Thank you!

Promoted to curated: I think concrete specific scenarios for how things might go with AI are IMO among the most helpful tools to help people start forming their own models about how this whole AI thing might go. Being specific is good, grounding things in concrete observable consequences is good. Somewhat sticking your neck out and making public predictions is good.

This is among the best entries I've seen in this genre, and I hope there will be more. Thank you for writing it!

but I know see that if you don't spend enough resources on addressing that hurt 

typo,  I "now" see that [,,,]

The remarkable thing about human genetics is that most of the variants ARE additive.

I think this is likely incorrect, at least where intelligence-affecting SNPs stacked in large numbers are concerned.

To make an analogy to ML, the effect of a brain-affecting gene will be to push a hyperparameter in one direction or the other. If that hyperparameter is (on average) not perfectly tuned, then one of the variants will be an enhancement, since it leads to a hyperparameter-value that is (on average) closer to optimal.

If each hyperparameter is affected by many genes (or, almost-equivalently, if the number of genes greatly exceeds the number of hyperparameters), then intelligence-affecting traits will look additive so long as you only look at pairs, because most pairs you look at will not affect the same hyperparameter, and when they do affect the same hyperparameter the combined effect still won't be large enough to overshoot the optimum. However, if you stack many gene edits, and this model of genes mapping to hyperparameters is correct, then the most likely outcome is that you move each hyperparameter in the correct direction but overshooting the optimum. Phrased slightly differently: intelligence-affecting genes may be additive on current margins, but not remain additive when you stack edits in this way.

To make another analogy: SNPs affecting height may be fully additive, but if the thing you actually care about is basketball-playing ability, there is an optimum amount of editing after which you should stop, because while people who are 2m tall are much better at basketball than people who are 1.7m tall, people who are 2.6m tall are cripples.

For this reason, even if all the gene-editing biology works out, you will not produce people in the upper end of the range you forecast.

You can probably somewhat improve this situation by varying the number of edits you do. Ie, you have some babies in which you edit a randomly selected 10% of known intelligence-affecting SNPs, some in which you've edited 20%, some 30%, and so on. But finding the real optimum will probably require understanding what the SNPs actually do, in terms of a model of brain biology, and understanding brain biology well enough to make judgment calls about that.

Sounds awesome, but also clearly dual use. 

Sorry about that! I am adding a donate link back to the frontpage sometime this week. Here is the link for now: https://www.lesswrong.com/donate [? · GW] 

Could you expand on what sense you have 'taken this into account' in your models? What are you expecting to achieve by editing non-causal SNPs?

The first paper I linked is about epistasic effects on the additivity of a QTLs for quantitative trait, specifically heading date in rice, so this is evidence for this sort of effect on such a trait.

The general problem is without a robust causal understanding of what an edit does it is very hard to predict what shorts of problem might arise from novel combinations of variants in a haplotype. That's just the nature of complex systems, a single incorrect base in the wrong place may have no effect or cause a critical cascading failure. You don't know until you test it or have characterized the system so well you can graph out exactly what is going to happen. Just testing it in humans and seeing what happens is eventually going to hit something detrimental. When you are trying to do enhancement you tend to need a positive expectation that it will be safe not just no reason to think it won't be. Many healthy people would be averse to risking good health for their kid, even at low probability of a bad outcome.