I stand by my comment, unless you can show a PGS where a 1 s.d. change currently does something big.
A 1SD change on a latent variable can have a big absolute risk effect for liability-threshold traits like schizophrenia depending on the pre-existing absolute risk / where one is on the latent spectrum.
(This is the nonlinearity of normal distributions and thin tails again—if the risk is ~0 SD, perhaps because there are 2 schizophrenic parents carrying a very high risk burden, then shifting a fraction of a SD drops the absolute risk down from 40% to 11% (pnorm(qnorm(0.4) - 1))*, because the density drops fast in the middle of the bell curve; if the risk is <-2SD because there is no family history of schizophrenia, then the exact same latent shift in SDs will drop the absolute risk from something like 0.8% to 0.5%, because you are already out in the thin tails where you can only go from ‘rare’ to ‘rare’. Same PGS+embryo-count, same latent shift, very different practical implications. This is also true of common dichotomous traits where everyone is at high risk, not merely specific families: for example, diabetes or heart disease. Since a quarter to a half of the population will get these, the latent risk is very high, and 1SD shift on it will have large practical consequences. Going from 30% risk of diabetes to 6% risk would make a big difference healthwise.)
* for selecting using solely the current 2020 SCZ PGS of 7%, out of 5 embryos you’d get ‘only’ −0.21 SDs at most, so for the double-SCZ-parent case, that’d drop from 40% to 32%. Nevertheless, considering how devastating schizophrenia can be, to themselves and everyone around them, I’d say that an absolute risk change of 8% is extremely valuable and ‘does something big’.
Genetic correlations: maybe, but we haven’t looked at genetic correlations for many things
Yes we have. We have genetic correlations for literally thousands of human traits. The UKBB alone lets you compute pairwise correlations over like 4k traits. And this also ignores that we especially have composite/index traits like longevity, SES, mental illness diagnoses, or self-reported health. It requires tremendous gymnastics to claim there is some hidden explosive correlation which somehow doesn’t show up in those global traits; obviously, even if selecting for IQ selected for some deadly disease that has entirely escaped measurement, that must be vastly outweighed by all the other deadly diseases it selects against, otherwise the net positive (genetic & phenotypic) correlation with all-cause mortality/longevity would not exist. It’d be nil, or the other direction.
(The genetic correlation argument is one of the first counterobjections everyone comes up with, but it requires an almost total ignorance of the genetic correlation literature to sustain. Which is why critics like Turley have resorted to either focusing solely on EDU because it has some negative correlates & high IGE they can hammer on while counting on a receptive audience which doesn’t know that EDU is extremely unrepresentative and a bait-and-switch for IQ; moving the goalposts about ‘efficacy’ even further; or just abandoning all the original counterarguments entirely and talking about “but we haven’t clinically validated embryo selection and it would take decades to do so and the PGSes might change”, which is both false (countless sibling comparisons prove they work, PGSes don’t change much over time, not for what people would select on) and a nifty catch-22 - you can’t ‘validate’ them if it’s been banned because they haven’t been validated...)
The only thing more valid than sibling comparisons is actually doing it. Actually doing it should add only an iota to your confidence in it being valid, because all it is is what siblings already are.
A 1SD change on a latent variable can have a big absolute risk effect for liability-threshold traits like schizophrenia depending on the pre-existing absolute risk / where one is on the latent spectrum.
(This is the nonlinearity of normal distributions and thin tails again—if the risk is ~0 SD, perhaps because there are 2 schizophrenic parents carrying a very high risk burden, then shifting a fraction of a SD drops the absolute risk down from 40% to 11% (
pnorm(qnorm(0.4) - 1))*, because the density drops fast in the middle of the bell curve; if the risk is <-2SD because there is no family history of schizophrenia, then the exact same latent shift in SDs will drop the absolute risk from something like 0.8% to 0.5%, because you are already out in the thin tails where you can only go from ‘rare’ to ‘rare’. Same PGS+embryo-count, same latent shift, very different practical implications. This is also true of common dichotomous traits where everyone is at high risk, not merely specific families: for example, diabetes or heart disease. Since a quarter to a half of the population will get these, the latent risk is very high, and 1SD shift on it will have large practical consequences. Going from 30% risk of diabetes to 6% risk would make a big difference healthwise.)* for selecting using solely the current 2020 SCZ PGS of 7%, out of 5 embryos you’d get ‘only’ −0.21 SDs at most, so for the double-SCZ-parent case, that’d drop from 40% to 32%. Nevertheless, considering how devastating schizophrenia can be, to themselves and everyone around them, I’d say that an absolute risk change of 8% is extremely valuable and ‘does something big’.
Yes we have. We have genetic correlations for literally thousands of human traits. The UKBB alone lets you compute pairwise correlations over like 4k traits. And this also ignores that we especially have composite/index traits like longevity, SES, mental illness diagnoses, or self-reported health. It requires tremendous gymnastics to claim there is some hidden explosive correlation which somehow doesn’t show up in those global traits; obviously, even if selecting for IQ selected for some deadly disease that has entirely escaped measurement, that must be vastly outweighed by all the other deadly diseases it selects against, otherwise the net positive (genetic & phenotypic) correlation with all-cause mortality/longevity would not exist. It’d be nil, or the other direction.
(The genetic correlation argument is one of the first counterobjections everyone comes up with, but it requires an almost total ignorance of the genetic correlation literature to sustain. Which is why critics like Turley have resorted to either focusing solely on EDU because it has some negative correlates & high IGE they can hammer on while counting on a receptive audience which doesn’t know that EDU is extremely unrepresentative and a bait-and-switch for IQ; moving the goalposts about ‘efficacy’ even further; or just abandoning all the original counterarguments entirely and talking about “but we haven’t clinically validated embryo selection and it would take decades to do so and the PGSes might change”, which is both false (countless sibling comparisons prove they work, PGSes don’t change much over time, not for what people would select on) and a nifty catch-22 - you can’t ‘validate’ them if it’s been banned because they haven’t been validated...)
So what is the “best” way to validate them, in your opinion? Is there anything better than sibling comparisons?
The only thing more valid than sibling comparisons is actually doing it. Actually doing it should add only an iota to your confidence in it being valid, because all it is is what siblings already are.