“1. The non-additive model analyzed in the paper requires significant shared environment correlations to mask the non-additivity and make it consistent with data that (at face value) support additivity. See Table 7 in the Supplement. This level of environmental effect is, in the cases of height and g, probably excluded by adoption studies, although it may still be allowed for many disease traits.

2. The criticisms in section 11 of Hill, Goddard, and Visscher (2008; also discussed previously here) are, to my mind, rather weak. To quote a string theorist friend: “It is nothing more than the calculus of words” In particular, I flat out disagree with the following (p.46 of the Supplement):…

http://infoproc.blogspot.co.nz/2012/01/phantom-heritability.html

Viruses have had eons of time; humans a few decades (of precision, longer vague study).
Virus structure is (partly) gene tools. Humans have hands, which work the (whatever), which works the (other thing), which was made (wherever), by (whoever else), etc.
Viruses have huge numbers. Humans think we have overpopulation.
Virus has nothing better to do, get it right or die. Humans have survived without genomics.
Humans are building a better HD every day. Virus carries its knowledge on its back.
Human intellect. Viruses don’t have the biggest heads.
Human communication. I’m not going to speculate here.

Knowability? In the long run we’ll know everything, I’m sure. But while time stays finite, the stories may be more “This is going to be hard, y’see, the thing is…” than popular science/medical genetics/drug development would like.

More seriously, I thought there were independent ways to evaluate the additivity of gene contributions – i.e. to determine whether epistasis plays a role or not. When Richard Plomin came to give a talk in Birmingham about IQ and heritability, I asked if the remarkable heritability of IQ (estimated from the much higher similarity between MZ than DZ twins) might not equivalently be caused by epistasis. I got a quite confident answer that IQ was pretty much entirely additive. I didn’t ask how this was arrived at.

I guess I missed something.

Indistinguishable, maybe (or even yes). But to *explain* a polygenic trait, it matters tremendously whether it is a sum of individual independent genes or a complex non-linear function of gene-gene interactions.

Not an expert here but when I looked rather attentively at the literature at some point, a pattern emerged (unless it was my own bias playing): every time people work with model organisms and look at things that are relatively easy to control and interpret, they find epistasis everywhere and dominant. In contrast, human genomics is dominated by computer scientists and former physicists who are just sure that a simple additive model is the only one that matters and that it explains everything (even when it does not, as is the case with height).

But this is the point — once you inbreed a model organism enough to look at the effects of one or two genes then you find epistasis. Otherwise there is so much variation in a typical human and human development is so plastic that (as a statistician rather than a computer scientist or physicist) the law of large numbers comes into play and the effect of lots of genes of small effect on complex traits gives a nice gaussian distribution, which is indistinguishable from what would be expected with many genes of small effect.

I would say this is stronger than an assumption: Data and Theory Point to Mainly Additive Genetic Variance for Complex Traits

Previous discussion from 08 (including James Crow dialogue, appropriately enough).

Myself, I’d be very surprised if vast majority of lowly bench scientists who work on metabolic pathways or complex protein interactions would not favor major role of epistasis in the strongest possible manner. It just makes so much sense – other genes are the “environment” with which a particular allele interacts to produce the exact phenotype. Some interactions are strong, some weak. (Typical case: single known mutation in a key protein produces a wide range of disease phenotypes, from deadly to mild).

I think non-zero cells in the third column could also be due to assortative mating (which inflates DZ correlations), shared environmental effects, or specific prenatal effects that can lower MZ correlations (for example).

But I agree on your take on this so far. But I’m still looking at more too.

I call GeneWatch “deniers” rather than “creationists” though.