In expectation, editing all those variants could decrease someone’s diabetes risk to negligible levels.
That seems to assume the effects remain linear throughout all the switch-flipping, and that there aren’t any harmful effects from stacking too many of a certain type of variant. How likely are those?
Yes, I slightly oversimplified this point for the sake of keeping it short.
The effects ARE linear in the sense that you can predict diabetes risk with a function f(x_1+x_2+x_3...) for all diabetes affecting variants. The actual shape of the curve is not a straight line though. It’s more like an exponential.
As far as plieotropy goes (one gene having multiple effects), a better approach would be to create genetic predictors for a large number of traits and prioritize edits in proportion to their effect on the trait of interest, their probability of being causal, and the severity of the condition.
But do I believe you could probably just do edits for one disease without a significant negative effect? Yes, so long as the resulting genome is within roughly the normal human range in terms of overall disease risk. There isn’t that much plieotropy to begin with, and the plieotropy that does exist tends to work in your favor; decreasing diabetes risk also tends to decrease risk of hypertension and heart disaease.
In the post you talked about editing all 237 loci to make diabetes negligible, but now you talk about the normal human range. I think that is more correct. Editing all 237 loci would leave the normal human range; the effect on diabetes would be unpredictable and the probability of bad effects likely. Not because of pleiotropy, but just the breakdown of a control system outside of its tested regime.
The reflected-sigmoidish part doesn’t occur when predicting with a linear combination of genetic variants, only when predicting with the percentiles. Since PGSes are normally distributed, converting them to percentiles puts them through a a sigmoidish function, and so you need a reflected-sigmoidish function to invert it.
The connection between the raw PGS is exponential, as can be seen in the top graphs (or more realistically it is presumably sigmoidal, but we’re on the exponential part of the curve).
That seems to assume the effects remain linear throughout all the switch-flipping, and that there aren’t any harmful effects from stacking too many of a certain type of variant. How likely are those?
Yes, I slightly oversimplified this point for the sake of keeping it short.
The effects ARE linear in the sense that you can predict diabetes risk with a function f(x_1+x_2+x_3...) for all diabetes affecting variants. The actual shape of the curve is not a straight line though. It’s more like an exponential.
As far as plieotropy goes (one gene having multiple effects), a better approach would be to create genetic predictors for a large number of traits and prioritize edits in proportion to their effect on the trait of interest, their probability of being causal, and the severity of the condition.
But do I believe you could probably just do edits for one disease without a significant negative effect? Yes, so long as the resulting genome is within roughly the normal human range in terms of overall disease risk. There isn’t that much plieotropy to begin with, and the plieotropy that does exist tends to work in your favor; decreasing diabetes risk also tends to decrease risk of hypertension and heart disaease.
In the post you talked about editing all 237 loci to make diabetes negligible, but now you talk about the normal human range. I think that is more correct. Editing all 237 loci would leave the normal human range; the effect on diabetes would be unpredictable and the probability of bad effects likely. Not because of pleiotropy, but just the breakdown of a control system outside of its tested regime.
The reflected-sigmoidish part doesn’t occur when predicting with a linear combination of genetic variants, only when predicting with the percentiles. Since PGSes are normally distributed, converting them to percentiles puts them through a a sigmoidish function, and so you need a reflected-sigmoidish function to invert it.
The connection between the raw PGS is exponential, as can be seen in the top graphs (or more realistically it is presumably sigmoidal, but we’re on the exponential part of the curve).