I'm running a model with a Cholesky decomposition on a set of latent variables for the Big Five trait. When I run the model, the estimated phenotypic covariance (A+C+E) matrix among the latent variables Big Five variables and the E matrix are fine, but the A and C matrices are both singular. The diagonal elements of C are all near zero, so I'm not surprised by that one. Several of the estimated genetic correlations for A are extremely high (.80-.90). I'm wondering if these results suggest some sort of misspecification or empirical under-identification, or if the issue is just sampling error making the genetic covariances spuriously high.