Genomic prediction is used in plant breeding to help find the best genotypes for selection. Here, the  accurate estimation of  predictive accuracy (PA) and heritability (H) is essential for genomic selection (GS). As in other applications,  field data are analyzed via regression models, which are known to lead to biased estimation when the normality premise is violated, biases that may translate into inaccurate H and PA estimates and negatively impact GS. Therefore, a robust analogue of a method from the literature used for H and PA estimation is presented. Both techniques are then compared through simulation.

(Joint work with Hans-Peter Piepho & Joseph O. Ogutu, Bioinformatics Unit, Institute of Crop Science, University of Hohenheim, Stuttgart, Germany)