I think this seems likely for schizophrenia (though there seem to be also many environmental ways to break that mechanism, like infections and drugs). And it should even more applicable to IQ, if we believe that general good functioning of a brain is a bigger target for mutations than whatever it is for just schizo (meaning-making or meaning-sensing?).
I can sort of see why the effect of common variants of genes should be additive. But why should the effect of stuff breaking down, i.e. rare bad mutations, be additive? Something rare is not expected and can't be planned for by other genes (other than building simple and multiple redundant mechanisms for the same function). Seems like a percolation, or broken phone -like thing. Enough defects and it's a completely different thing. For the brain it is easy to imagine a model where just slightly bigger error rates in different parts would multiply for a big effect.
Furthermore, as Scott wrote, GWASes can't see rare mutations (unlike accurate whole genome sequencing), and they can't resolve the effect new mutations well, which we should expect bad, rare ones to be.
For the missing heritability of disease there could also be another reason: MZ twins go together like shirt hem and butt. They must have a smaller non-shared environment. Even when raised apart they seem to have some kind of frequency lock with each other. This would result in them having similar environmental insults, most importantly pathogenic ones.
But of course non-additive genetic errors in immune system coverage (susceptibility) and regulation (autoimmunity) could be important, too.
As an example: We know by Ewald, Cochran & Cochran that endometriosis (a very common and fertility-damaging disease) can't be genetic (unless a ton of different mutations can cause this quite specific disorder, not plausible). It is believed to be partly hereditary, which is not strictly wrong if we consider microbiome part of the inheritance. OTOH, raised together MZ twins are about 50% concordant, which is considerably higher than just any pair of sisters or DZ twins raised together. But when they tried to build a genomic predictor for endometriosis (https://www.nature.com/articles/s41588-023-01323-z), they found
1. they can explain 5% of the variance
2. "significant comorbidity with other pain disorders", which I believe translates to "mostly confounded" =)
There's a small chance this genetic non-linearity thing could be the highest value application of ML (prioritizing where you put that selective effort), if it works. No need to cringe.
I think this seems likely for schizophrenia (though there seem to be also many environmental ways to break that mechanism, like infections and drugs). And it should even more applicable to IQ, if we believe that general good functioning of a brain is a bigger target for mutations than whatever it is for just schizo (meaning-making or meaning-sensing?).
I can sort of see why the effect of common variants of genes should be additive. But why should the effect of stuff breaking down, i.e. rare bad mutations, be additive? Something rare is not expected and can't be planned for by other genes (other than building simple and multiple redundant mechanisms for the same function). Seems like a percolation, or broken phone -like thing. Enough defects and it's a completely different thing. For the brain it is easy to imagine a model where just slightly bigger error rates in different parts would multiply for a big effect.
Furthermore, as Scott wrote, GWASes can't see rare mutations (unlike accurate whole genome sequencing), and they can't resolve the effect new mutations well, which we should expect bad, rare ones to be.
For the missing heritability of disease there could also be another reason: MZ twins go together like shirt hem and butt. They must have a smaller non-shared environment. Even when raised apart they seem to have some kind of frequency lock with each other. This would result in them having similar environmental insults, most importantly pathogenic ones.
But of course non-additive genetic errors in immune system coverage (susceptibility) and regulation (autoimmunity) could be important, too.
As an example: We know by Ewald, Cochran & Cochran that endometriosis (a very common and fertility-damaging disease) can't be genetic (unless a ton of different mutations can cause this quite specific disorder, not plausible). It is believed to be partly hereditary, which is not strictly wrong if we consider microbiome part of the inheritance. OTOH, raised together MZ twins are about 50% concordant, which is considerably higher than just any pair of sisters or DZ twins raised together. But when they tried to build a genomic predictor for endometriosis (https://www.nature.com/articles/s41588-023-01323-z), they found
1. they can explain 5% of the variance
2. "significant comorbidity with other pain disorders", which I believe translates to "mostly confounded" =)
There's a small chance this genetic non-linearity thing could be the highest value application of ML (prioritizing where you put that selective effort), if it works. No need to cringe.
I’m completely convinced, the problem is that we lack the data/tools to find nonlinear patterns