Under the condition I mentioned, polygenic scores will tend to focus on the traits that cause the most common kind of depression, while neglecting other kinds. The missing heritability will be due to missing those other kinds.
I don’t get why you think this. It doesn’t seem to make any sense. You’d still notice the effect of variants that cause depression-rare, exactly like if depression-rare was the only kind of depression. How is your ability to detect depression-rare affected by the fact that there’s some genetic depression-common? Depression-common could just as well have been environmentally caused.
I might be being dumb, I just don’t get what you’re saying and don’t have a firm grounding myself.
It doesn’t matter if depression-common is genetic or environmental. Depression-common leads to the genetic difference between your cases and controls to be small along the latent trait axis that causes depression-rare. So the effect gets estimated to be not-that-high. The exact details of how it fails depends on the mathematical method used to estimate the effect.
Ok I think I get what you’re trying to communicate, and it seems true, but I don’t think it’s very relevant to the missing heritability thing. The situation you’re describing applies to the fully linear case too. You’re just saying that if a trait is more polygenic / has more causes with smaller effects, it’s harder to detect relevant causes. Unless I still don’t get what you’re saying.
It kind-of applies to the Bernoulli-sigmoid-linear case that would usually be applied to binary diagnoses (but only because of sample size issues and because they usually perform the regression one variable at a time to reduce computational difficulty), but it doesn’t apply as strongly as it does to the polynomial case, and it doesn’t apply to the purely linear (or exponential-linear) case at all.
If you have a purely linear case, then the expected slope of a genetic variant onto an outcome of interest is proportional to the effect of the genetic variant.
The issue is in the polynomial case, the effect size of one genetic variant depends on the status of other genetic variants within the same term in the sum. Statistics gives you a sort of average effect size, but that average effect size is only going to be accurate for the people with the most common kind of depression.
Not right now, I’m on my phone. Though also it’s not standard genetics math.
Ok.
I don’t get why you think this. It doesn’t seem to make any sense. You’d still notice the effect of variants that cause depression-rare, exactly like if depression-rare was the only kind of depression. How is your ability to detect depression-rare affected by the fact that there’s some genetic depression-common? Depression-common could just as well have been environmentally caused.
I might be being dumb, I just don’t get what you’re saying and don’t have a firm grounding myself.
It doesn’t matter if depression-common is genetic or environmental. Depression-common leads to the genetic difference between your cases and controls to be small along the latent trait axis that causes depression-rare. So the effect gets estimated to be not-that-high. The exact details of how it fails depends on the mathematical method used to estimate the effect.
Ok I think I get what you’re trying to communicate, and it seems true, but I don’t think it’s very relevant to the missing heritability thing. The situation you’re describing applies to the fully linear case too. You’re just saying that if a trait is more polygenic / has more causes with smaller effects, it’s harder to detect relevant causes. Unless I still don’t get what you’re saying.
It kind-of applies to the Bernoulli-sigmoid-linear case that would usually be applied to binary diagnoses (but only because of sample size issues and because they usually perform the regression one variable at a time to reduce computational difficulty), but it doesn’t apply as strongly as it does to the polynomial case, and it doesn’t apply to the purely linear (or exponential-linear) case at all.
If you have a purely linear case, then the expected slope of a genetic variant onto an outcome of interest is proportional to the effect of the genetic variant.
The issue is in the polynomial case, the effect size of one genetic variant depends on the status of other genetic variants within the same term in the sum. Statistics gives you a sort of average effect size, but that average effect size is only going to be accurate for the people with the most common kind of depression.