  Inferring a graphical model or network from observational data from a large
number of variables is a well studied problem in machine learning and
computational statistics. In this paper we consider a version of this problem
that is relevant to the analysis of multiple phenotypes collected in genetic
studies. In such datasets we expect correlations between phenotypes and between
individuals. We model observations as a sum of two matrix normal variates such
that the joint covariance function is a sum of Kronecker products. This model,
which generalizes the Graphical Lasso, assumes observations are correlated due
to known genetic relationships and corrupted with non-independent noise. We
have developed a computationally efficient EM algorithm to fit this model. On
simulated datasets we illustrate substantially improved performance in network
reconstruction by allowing for a general noise distribution.
