Using simulation, it is shown that the profile likelihood function provides more appropriate estimates of confidence intervals than large sample variances.
In particular, large sample variances and sampling correlations are demonstrated to provide an indication of ‘problem’ scenarios. This is illustrated for the example of a design suggested recently to estimate X-linked genetic variances. It is shown that likelihood-based calculations may provide insight into the quality of the resulting parameter estimates, and are directly applicable to the validation of experimental designs. Models involving multiple genetic variance components, due to different modes of gene action, are readily fitted. Mixed model analyses via restricted maximum likelihood, fitting the so-called animal model, have become standard methodology for the estimation of genetic variances.
