Genetic Correlation - Computing The Genetic Correlation

Computing The Genetic Correlation

Estimates of a genetic correlation obviously require a genetically informative sample, such as a twin study.

Given a genetic covariance matrix, the genetic correlation is computed by standardizing this, i.e., by converting the covariance matrix to a correlation matrix. For example, if two traits, say height and weight have the following additive genetic variance-covariance matrix:

Then the genetic correlation is .55, as seen is the standardized matrix below:

In practice, structural equation modeling applications such as OpenMx are used to calculate both the genetic covariance matrix and its standardized form. In R, cov2cor will standardize the matrix.

Typically, published reports will provide genetic variance components that have been standardized as a proportion of total variance (for instance in an ACE twin study model standardised as a proportion of V-total = A+C+E). In this case, the metric for computing the genetic covariance (the variance within the genetic covariance matrix) is lost (because of the standardizing process), so you cannot readily estimate the genetic correlation of two traits from such published models. Multivariate models (such as the Cholesky decomposition) will, however, allow the viewer to see shared genetic effects (as opposed to the genetic correlation) by following path rules. it is important therefore to provide the unstandardised path coefficients in publications.