Behavior of the Linear Regression method to estimate bias and accuracies with correct and incorrect genetic evaluation models

Bias in genetic evaluations has been a constant concern in animal genetics. The interest in this topic has increased in the last years, since many studies have detected overestimation (bias) in estimated breeding values (EBV). Detecting the existence of bias, and the realized accuracy of predictions, is therefore of importance, yet this is difficult when studying small data sets or breeds. In this study, we tested by simulation the recently presented method Linear Regression (LR) for estimation of bias, slope, and accuracy of pedigree EBV. The LR method computes statistics by comparing EBV from a data set containing old, partial information with EBV from a data set containing all information (old and new, a whole data set) for the same individuals. The method proposes an estimator for bias p ( ) , an estimator of slope b p ( ) , and 3 estimators re- lated to accuracies: the ratio between accuracies , , w p ( ) the reliability of the partial data set acc p 2 , and the ratio of reliabilities p w , . 2 We simulated a dairy scheme