Antagonistic pleiotropy with unmeasured traits. Some crucial traits, such as what is called Wisdom and what is called Kindness, might not be feasibly measurable with a PGS and therefore can’t be used as a component in a weighted mixture of PGSes used for genomic engineering. If there is antagonistic pleiotropy between those traits and traits selected for by GE, they’ll be decreased.
A related issue is that intelligence itself could affect personality:
Even if a trait is accurately measured by a PGS and successfully increased by GE, the trait may have unmapped consequences, and thus may be undesirable to the parents and/or to the child. For example, enhancing altruistic traits might set the child up to be exploited by unscrupulous people.
An example with intelligence is that very intelligent people might tend to be isolated, or might tend to be overconfident (because of not being corrected enough).
One practical consideration is that sometimes PGSes are constructed by taking related phenotypes and just using those because they correlate. The big one for IQ is Educational Attainment, because EA is easier to measure than IQ (you just ask about years of schooling or whatever). If you do this in the most straightforward way, you’re just selecting for EA, which would probably select for several personality traits, some maybe undesirable.
I think in practice these effects will probably be pretty small and not very concerning, though we couldn’t know for sure without trying and seeing. A few lines of reasoning:
Correlations between IQ and known personality traits are either very small or pretty small. You could look at https://en.wikipedia.org/wiki/Intelligence_and_personality . The numbers there are usually less than .3 or even .2, in absolute value. If a correlation is .25, that means 4 SDs of IQ translates to 1 SD on that trait (a priori). That means a 1 in 20,000 exceptionally smart kid is 1 in 6 exceptional on that trait. You could notice, but I think it would be mild. Of course, this could be different for unknown personality traits; but scientists IIUC do try to find general factors in tests, so it would have to be something that doesn’t show up there.
Most trait correlations in general seem to be quite small. See https://www.youtube.com/watch?v=n64rrRPtCa8&t=1620s Of course this is determined by what traits we’re talking about, and as we see above, the traits are correlated. But what this says to me is that even highly polygenic traits that are vaguely related (e.g. varous health things; or, intelligence vs. mental illness) can easily be mostly disjoint—in fact by default they usually are. In other words, if there’s a significant correlation between two traits, I would guess that it’s not so much “really due to pleiotropy”, but rather due to the traits being somehow actually overlapping. I think that would suggest you get roughly the same sort of distribution as you see empirically today; in other words, there wouldn’t be surprise genetic pleiotropy. (I’m not sure this argument makes sense, I haven’t thought about it much.)
There’s a huge amount of genetic variation in IQ to select from. See https://tsvibt.blogspot.com/2022/08/the-power-of-selection.html#7-the-limits-of-selection . This means that there’s actually a huge range of ways to add 50 IQ points by making genetic tweaks. Just to illustrate the point with fake numbers, suppose that IQ is the sum of 10,000 fair coin flips (some genetic, some environmental); a standard deviation is then 50. And suppose we know 1000 of them. That’s already 1000 / 50 = 20 SDs! There’s a lot of ways to pick 150 from 1000, and there’s still a lot of ways if you enforce some substantial disoverlap between all pairs of subsets.
From the subjective perspective of an unmodified human, these changes are likely to be “for the worse.”
If you pick your child’s genes to maximize their IQ (or any other easily-measurable metric), you might end up with the human equivalent of a benchmaxxed LLM with amazing test scores but terrible vibes.
I’m not sure I follow. I mean I vaguely get it, but I don’t non-vaguely get it.
And in the case of superbabies, we’d have to wait decades to find out what they’re like once they’ve grown up.
I don’t think this is right. If we’re talking about selection (rather than editing), the child has a genome that is entirely natural, except that it’s selected according to your PGS to be exceptional on that PGS. This should be basically exactly the same as selecting someone who is exceptional on your PGS from the population of living people. So you could just look at the tails of your PGS in the population and see what they’re like. (This does become hard with traits that are rare / hard / expensive to measure, and it’s hard if you’re interested in far tails, like >3 SDs say.) (In general, tail studies seem underattended; see https://www.lesswrong.com/posts/i4CZ57JyqqpPryoxg/some-reprogenetics-related-projects-you-could-help-with , though also see https://pmc.ncbi.nlm.nih.gov/articles/PMC12176956/ which might be some version of this (for other traits).)
It’s a concern. Several related issues are mentioned here: https://berkeleygenomics.org/articles/Potential_perils_of_germline_genomic_engineering.html E.g. search “personality” and “values”, and see:
A related issue is that intelligence itself could affect personality:
An example with intelligence is that very intelligent people might tend to be isolated, or might tend to be overconfident (because of not being corrected enough).
One practical consideration is that sometimes PGSes are constructed by taking related phenotypes and just using those because they correlate. The big one for IQ is Educational Attainment, because EA is easier to measure than IQ (you just ask about years of schooling or whatever). If you do this in the most straightforward way, you’re just selecting for EA, which would probably select for several personality traits, some maybe undesirable.
I think in practice these effects will probably be pretty small and not very concerning, though we couldn’t know for sure without trying and seeing. A few lines of reasoning:
Correlations between IQ and known personality traits are either very small or pretty small. You could look at https://en.wikipedia.org/wiki/Intelligence_and_personality . The numbers there are usually less than .3 or even .2, in absolute value. If a correlation is .25, that means 4 SDs of IQ translates to 1 SD on that trait (a priori). That means a 1 in 20,000 exceptionally smart kid is 1 in 6 exceptional on that trait. You could notice, but I think it would be mild. Of course, this could be different for unknown personality traits; but scientists IIUC do try to find general factors in tests, so it would have to be something that doesn’t show up there.
Most trait correlations in general seem to be quite small. See https://www.youtube.com/watch?v=n64rrRPtCa8&t=1620s Of course this is determined by what traits we’re talking about, and as we see above, the traits are correlated. But what this says to me is that even highly polygenic traits that are vaguely related (e.g. varous health things; or, intelligence vs. mental illness) can easily be mostly disjoint—in fact by default they usually are. In other words, if there’s a significant correlation between two traits, I would guess that it’s not so much “really due to pleiotropy”, but rather due to the traits being somehow actually overlapping. I think that would suggest you get roughly the same sort of distribution as you see empirically today; in other words, there wouldn’t be surprise genetic pleiotropy. (I’m not sure this argument makes sense, I haven’t thought about it much.)
There’s a huge amount of genetic variation in IQ to select from. See https://tsvibt.blogspot.com/2022/08/the-power-of-selection.html#7-the-limits-of-selection . This means that there’s actually a huge range of ways to add 50 IQ points by making genetic tweaks. Just to illustrate the point with fake numbers, suppose that IQ is the sum of 10,000 fair coin flips (some genetic, some environmental); a standard deviation is then 50. And suppose we know 1000 of them. That’s already 1000 / 50 = 20 SDs! There’s a lot of ways to pick 150 from 1000, and there’s still a lot of ways if you enforce some substantial disoverlap between all pairs of subsets.
Glancing at the correlations given in the wiki page ( https://en.wikipedia.org/wiki/Intelligence_and_personality ) I don’t especially feel that way.
I’m not sure I follow. I mean I vaguely get it, but I don’t non-vaguely get it.
I don’t think this is right. If we’re talking about selection (rather than editing), the child has a genome that is entirely natural, except that it’s selected according to your PGS to be exceptional on that PGS. This should be basically exactly the same as selecting someone who is exceptional on your PGS from the population of living people. So you could just look at the tails of your PGS in the population and see what they’re like. (This does become hard with traits that are rare / hard / expensive to measure, and it’s hard if you’re interested in far tails, like >3 SDs say.) (In general, tail studies seem underattended; see https://www.lesswrong.com/posts/i4CZ57JyqqpPryoxg/some-reprogenetics-related-projects-you-could-help-with , though also see https://pmc.ncbi.nlm.nih.gov/articles/PMC12176956/ which might be some version of this (for other traits).)