Technical Note: Calculation of standard errors of estimates of genetic parameters with the MTDFREML programs

¹ Department of Statistics, University of Nebraska, Lincoln 68583-0963
² USDA, ARS, Roman L. Hruska U.S. Meat Animal Research Center, Lincoln, NE 68583-0908

^* To whom correspondence should be addressed. E-mail: lvanvleck{at}unlnotes.unl.edu.

	Abstract

The MTDFREML set of programs were written to handle partially missing data for multiple-trait analyses as well as single trait models. Standard errors of genetic parameters were reported for univariate models and for multiple-trait analyses only when all traits were measured on animals with records. In addition to estimating (co)variance components for multiple trait models with partially missing data, this paper shows how the MTDFREML set of programs can also estimate standard errors when not all animals have all traits measured by augmenting the data file. While the standard practice has been to eliminate records with partially missing data, that practice uses only a subset of the available data. In some situations the elimination of partial records can result in elimination of all the records such as one trait measured in one environment and a second trait measured in a different environment. An alternative approach requiring minor modifications of the original data and model was developed that provides estimates of the SE for multiple trait analyses when not all traits are measured using an augmented data set that gives the same residual log likelihood as the original data. Because the same residual vector is used for the original data and the augmented data, the resulting REML estimators along with their sampling properties are identical for the original and augmented data so that SE for estimates of genetic parameters can be calculated.

Key Words: average information matrix, genetic parameters, REML, standard errors