Andrew Gelman sometimes writes that in genetics it might make sense to have a null hypothesis of zero effect, but in social science nothing is ever exactly zero (and interactions abound). I wonder whether that is actually true even for genetics. Think about pleiotropy. Be it universal or modular, I think the evidence still points in the direction that we should expect any genetic variant to affect lots of traits, albeit with often very small effects. And think of gene expression where genes always show lots of correlation structure: do we expect transcripts from the same cells to ever be independent of each other? It doesn’t seem to me that the null can be strictly true here. Most of these differences have to be too small for us to practically be able to model them, though — and maybe the small effects are so far below the detection limit that we can pretend that they could be zero. (Note: not trying to criticise anybody’s statistical method or view of effect sizes here, just thinking aloud about the ”no true null effect” argument.)
In the simplest terms pleiotropy means genetic side-effects: a pleiotropic gene is a gene that does several things and a pleiotropic variant is a variant that makes its carrier different from carriers of other variants in more than one trait. It’s just that the words ‘gene’ , ‘trait’ and ‘different’ are somewhat ambiguous. Paaby & Rockman (2013) have written a nice analytical review about the meaning of pleiotropy. In their terminology, molecular gene pleiotropy is when the product of a gene is involved in more than one biological process. Developmental pleiotropy, on the other hand, deals with genetic variants: a variant is developmentally pleiotropic if it affects more than one trait. This is the sense of the word I’d normally think of. Third, selectional pleiotropy is deals with variants that affect several aspects of fitness, possibly differently for different individuals.
Imagine that we have found a variant associated with two variables. Have we got a pleiotropic variant on our hands? If the variables are just different measures of the same thing, clearly we’re dealing with one trait. But imagine that the variables are actually driven by largely different factors. They might respond to different environmental stimuli and have mostly separate genetic architectures. If so, we have two different traits and a pleiotropic variant affecting both. My point is that it depends on the actual functional relationship between the traits. Without knowing something about how the organism works we can’t count traits. With that in mind, it seems very bold to say things about variants in general and traits in general. Paaby & Rockman’s conclusion seems to be that genetic mapping is not the way to go, because of low power to detect variants of small effect, and instead they bring up alternative statistical and quantitative genetics methods to demonstrate pleiotropy on a large scale. I agree that these results reinforce that pleiotropy must be important, in some sense of the word. But I think the opposite approach still has value: the way to figure out how important pleiotropy is for any given suite of traits is to study them mechanistically.
(Zombie kitty by Anna Nygren.)