The posts this week have been about epigenetics. However, let’s step back from the molecular mechanisms and what not to look at the bigger picture. This recent paper by Noble, McFarlane, Keogh and Whiting (2014) looks at maternal effects and additive genetic effects on fitness-related traits in a lizard. Now we are in quantitative genetics territory where one uses pedigrees and phenotypes to look at the determinants of a trait while abstracting away the mechanistic details. Nowadays, quantitative genetics is also equipped with Bayesian animal models and the ability to do parentage assignment with molecular methods.
The authors measured at size, body mass, and growth and as well as the speed and endurance when running. The fun part is that while only endurance had a substantial heritability (0.4), the other traits had maternal components in the 0.2-0.5 range. So for most of the traits there’s little heritability while a big chunk of the trait variance is explained by maternal effects.
I like the idea to include maternal traits to see look at what causes the maternal effect. Clutch size, maternal size and condition seem matter for some trait or another. In two cases the maternal effect is entirely explained away: the effect on growth by birth date and clutch size, and sprint speed by birth date.
The inferences come from an animal model that include a maternal effect. Something I’m curious about is how heritability would be overestimated if the maternal component was not accounted for. That is beside the point of the paper, though.
Another interesting point: I think everyone who deals with animals in some type of controlled environment wonder about how much our measurements differ from what would’ve been measured in a more natural environment. In this case, the authors measured offspring growth both in the test environment and in an enclosure. They find a maternal effect in the test environment, while the interval for the heritability goes from almost zero to 0.5. In the wilder environment they estimate very little genetic and maternal variance, as well as a larger residual variance. I don’t know if this is just because of increased noise, or because maternal effects actually interact with condition.
Also, I love figure 1 (the one figure). If more papers had caterpillar plots of most important estimated quantities, the world would be a better place.