There’s no secret sauce here. Just take a lot of samples and run a regression.
Pretend for a second it’s a nutrition study and apply your usual scepticism :-) You know quite well that “just run a regression” is, um… rarely that simple.
To give one obvious example, interaction effects are an issue, including interaction between genes and the environment.
Pretend for a second it’s a nutrition study and apply your usual scepticism :-) You know quite well that “just run a regression” is, um… rarely that simple.
No, that’s the great thing about genetic associations! First, genes don’t change over a lifetime, so every association is in effect a longitudinal study where the arrow of time immediately rules out A<-B or reverse causation in which IQ somehow causes particular variants to be overrepresented; that takes out one of the three causal pathways. Then you’re left with confounding—but there’s almost no way for a third variable to pick out people with particular alleles and grant them higher intelligence, no greenbeard effect, and population differences are dealt with by using relatively homogenous samples & controlling for principal components—so you don’t have to worry much about A<-C->B. So all you’re left with is A->B.
To give one obvious example, interaction effects are an issue, including interaction between genes and the environment.
But they’re not. They’re not a large part of what’s going on. And they don’t affect the associations you find through a straight analysis looking for additive effects.
Pretend for a second it’s a nutrition study and apply your usual scepticism :-) You know quite well that “just run a regression” is, um… rarely that simple.
To give one obvious example, interaction effects are an issue, including interaction between genes and the environment.
No, that’s the great thing about genetic associations! First, genes don’t change over a lifetime, so every association is in effect a longitudinal study where the arrow of time immediately rules out A<-B or reverse causation in which IQ somehow causes particular variants to be overrepresented; that takes out one of the three causal pathways. Then you’re left with confounding—but there’s almost no way for a third variable to pick out people with particular alleles and grant them higher intelligence, no greenbeard effect, and population differences are dealt with by using relatively homogenous samples & controlling for principal components—so you don’t have to worry much about A<-C->B. So all you’re left with is A->B.
But they’re not. They’re not a large part of what’s going on. And they don’t affect the associations you find through a straight analysis looking for additive effects.
But their expression does.
How do you know?
An expression in circumstances dictated by what genes one started with.
Because if they were a large part of what was going on, the estimates would not break down cleanly and the methods work so well.