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.
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’m curious about the basis on which you are assigning a probability of causality without a method like mendelian randomisation, or something that tries to assign a probability of an effect based on interpreting the biology like a coding of the output of something like SnpEff to an approximate probability of effect.
The logic of 30% of its effect based on 30% chance it’s causal only seems like it will be pretty high variance and only work out over a pretty large number of edits. It is also assuming no unexpected effects of the edits to SNPs that are non-causal for whatever trait you are targeting but might do something else when edited.
I’m curious about the basis on which you are assigning a probability of causality without a method like mendelian randomisation, or something that tries to assign a probability of an effect based on interpreting the biology like a coding of the output of something like SnpEff to an approximate probability of effect.
Using finemapping. I.e. assuming a model where nonzero additive effects are sparsely distributed among SNPs, you can do Bayesian math to infer how probable each SNP is to have a nonzero effect and its expected effect size conditional on observed GWAS results. Things like SnpEff can further help by giving you a better prior.
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.
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.
I didn’t read your first comment carefully enough; I’ll take a look at this.
Can you comment your current thoughts on rare haplotypes?
Don’t have much to say on it right now, I really need to do a deep dive into this at some point.
I’m curious about the basis on which you are assigning a probability of causality without a method like mendelian randomisation, or something that tries to assign a probability of an effect based on interpreting the biology like a coding of the output of something like SnpEff to an approximate probability of effect.
The logic of 30% of its effect based on 30% chance it’s causal only seems like it will be pretty high variance and only work out over a pretty large number of edits. It is also assuming no unexpected effects of the edits to SNPs that are non-causal for whatever trait you are targeting but might do something else when edited.
Using finemapping. I.e. assuming a model where nonzero additive effects are sparsely distributed among SNPs, you can do Bayesian math to infer how probable each SNP is to have a nonzero effect and its expected effect size conditional on observed GWAS results. Things like SnpEff can further help by giving you a better prior.
(For people reading this thread who want an intro to finemapping this lecture is a great place to start for a high level overview https://www.youtube.com/watch?v=pglYf7wocSI)