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.
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)