You don’t need to understand the mechanism of action. You don’t need an animal model of disease. You just need a reasonable expectation that changing a genetic variant will have a positive impact on the thing you care about. And guess what? We already have all that information. We’ve been conducting genome-wide association studies for over a decade.
This is critically wrong, because association is uninformative about interventions.
Genome-wide association studies are enormously informative about correlations in a particular environment. In a complex setting like genomics, and where there are systematic correlations between environmental conditions and population genetics to confound your analysis, this doesn’t give you much reason to think that interventions will have the desired effect!
I’d recommend reading up on causal statistics; Pearl’s Book of Why is an accessible introduction and the wikipedia page is also reasonably good.
I’m happy to read any evidence to the contrary, but my current view is that for most traits, the genetic variants that only have effects in certain environments that are no longer present are pretty unusual. And even in the cases where this is true, it will only slightly reduce the effect size from editing.
You’re correct about GWAS only showing associations, but there are ways around this. For one thing, you can use sibling validation to narrow down the set of variants to direct causal impacts rather than genetic nurture, for example, and you can use finemapping and other techniques to reduce uncertainty about which SNP in a clump is actually causing the effect.
And after all that, you can just sort the genes to edit by effect size * probability of being causal when deciding edit priority.
Ok, so, suppose that pretty much everyone who grew up in Britain in the 1960s has 100 identifiable genetic variants that pretty much no one else has, and by coincidence there were some schools in Britain in the 1960s that taught a bunch of awful dietary habits to schoolchildren, and so there’s a major correlation between those variants and developing diabetes. It also happens that 10 of those variants actually make the body more susceptible to diabetes, and 10 of them protect against diabetes, and the rest have zero causal effect. @GeneSmith , what would the GWAS conclude in this case, and how common and important are cases like it? (E.g. I would guess there are a bunch of genes that correlate with socioeconomic status but aren’t causal.)
This is where sibling validation becomes very useful: if the observed effect is due an actual direct genetic effect, it will allow you to distinguish siblings with higher and lower diabetes risk. If it’s due to some weird environmental gene correlation, it won’t.
If you did the sibling testing you would find that 10 of the associated genes replicated and 90 did not.
It seems implausible that everyone who grew up in Britain in the 1960s would have genetic variants that no one else has. Their parents and children would have grown up in different decades, whether in Britain or elsewhere, and they would also have those variants.
This is critically wrong, because association is uninformative about interventions.
Genome-wide association studies are enormously informative about correlations in a particular environment. In a complex setting like genomics, and where there are systematic correlations between environmental conditions and population genetics to confound your analysis, this doesn’t give you much reason to think that interventions will have the desired effect!
I’d recommend reading up on causal statistics; Pearl’s Book of Why is an accessible introduction and the wikipedia page is also reasonably good.
I’m happy to read any evidence to the contrary, but my current view is that for most traits, the genetic variants that only have effects in certain environments that are no longer present are pretty unusual. And even in the cases where this is true, it will only slightly reduce the effect size from editing.
You’re correct about GWAS only showing associations, but there are ways around this. For one thing, you can use sibling validation to narrow down the set of variants to direct causal impacts rather than genetic nurture, for example, and you can use finemapping and other techniques to reduce uncertainty about which SNP in a clump is actually causing the effect.
And after all that, you can just sort the genes to edit by effect size * probability of being causal when deciding edit priority.
Ok, so, suppose that pretty much everyone who grew up in Britain in the 1960s has 100 identifiable genetic variants that pretty much no one else has, and by coincidence there were some schools in Britain in the 1960s that taught a bunch of awful dietary habits to schoolchildren, and so there’s a major correlation between those variants and developing diabetes. It also happens that 10 of those variants actually make the body more susceptible to diabetes, and 10 of them protect against diabetes, and the rest have zero causal effect. @GeneSmith , what would the GWAS conclude in this case, and how common and important are cases like it? (E.g. I would guess there are a bunch of genes that correlate with socioeconomic status but aren’t causal.)
This is where sibling validation becomes very useful: if the observed effect is due an actual direct genetic effect, it will allow you to distinguish siblings with higher and lower diabetes risk. If it’s due to some weird environmental gene correlation, it won’t.
If you did the sibling testing you would find that 10 of the associated genes replicated and 90 did not.
It seems implausible that everyone who grew up in Britain in the 1960s would have genetic variants that no one else has. Their parents and children would have grown up in different decades, whether in Britain or elsewhere, and they would also have those variants.