A note on the effect of observations with missing data on genetic correlation estimates

Abstract

Various studies have estimated covariance components as half the difference between the variance component of the sum of the variable values, for each observation, and the sum of the corresponding variable variance components. Although the variance components for the separate variables can be computed using all available data, the variance components of the sum can be computed only from those observations with records for both variables. Previous studies have suggested eliminating observations with missing data, because of possible selection bias. The effect of missing data on estimates of covariance components and genetic correlations was tested on sample beef cattle data and simulated data by randomly deleting differing proportions of records of one variable for each pair of variables analyzed. Estimates of genetic correlations computed with observations with missing data eliminated, were more accurate than estimates computed using all available data. Furthermore, when observations with missing data were included, estimates of genetic correlation far outside the parameter space were common. Therefore, this method should be used only if observations with missing data have been eliminated.