The variation in a population can be quantified using statistics related to the Normal Distribution and is expressed as a so-called variance component. In symbols this generally is indicated with a σ2. So the phenotypic variance is called σ2P, the genetic variance is called σ2G, and the environmental variance is called σ2E. Our model of P = G + E is also applicable to variance components:
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Variation in a population is expressed in a variance component. The symbol for a variance component is σ2 and the subscript indicates what type of variance component it is: P, G, or E. |
For estimating these variance components, we make use of the fact that if a trait is heritable it would mean that brothers and sisters perform more alike than unrelated individuals. So we combine the phenotypic information on the animals with their genetic relationships (i.e. the pedigree), and then the only component we have no real information about is the environment. Of course we can identify certain components of the environment, like housing and nutrition. But because the influence of the environment already starts at conception, we cannot identify all components of the environment. And some components we are not even aware of, like the potential influence of the weather three weeks ago on the performance today. We can estimate σ2E by subtracting σ2G from σ2P. So E = P – G. Because this is not a very accurate way of estimating the variance due to environmental influences, this variance component is called error variance, rather than environmental variance.
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