Modeling missing parents in single-step test-day SNP-BLUP evaluation of dairy cattle

In many countries, single-step genomic models have replaced multiple step models for routine evaluation. These models use all available information on animals phenotypes, genotypes, and pedigrees, yet missing parental information in pedigrees remains a challenge that affects genomic breeding value (GEBV) predictions. Therefore, the choice of method for handling missing parents can affect the prediction of breeding values. Here, we compared three approaches to model missing parental information for three levels of missing pedigree data: P_Real - pedigree from routine evaluation, P_2010 - at least 20 percent of dams and 10 percent of sires born before 2019 were set to missing, and P_4020 - at least 40 percent of dams and 20 percent of sires born before 2019 were set to missing. Missing parents information was expressed through missing codes in the raw pedigree (RP) by defining genetic groups (GG) that represent missing parents grouped based on year of birth, sex, and country of origin, or by defining metafounders (MF), which represent missing parents grouped by average genetic relationships estimated from the genomic information of their descendants. The genomic breeding values for fat yield were estimated using the single-step test-day SNP-BLUP model implemented with MiXBLUP software. For the considered scenarios, the results were presented separately for sires and dams, as well as for genotyped and ungenotyped individuals. We observed differences in the prediction quality between genotyped and ungenotyped animals. While GEBV predictions for the former were generally stable across scenarios, the predictions for the ungenotyped individuals varied. In particular, the removal of parental information led to less stable results when missing parental information was expressed by MF, where insufficient pedigree completeness resulted in an overestimation of the genetic trend. In conclusion, for informative pedigrees with a small percentage of missing parents, the incorporation of GG and MF results in very similar GEBV predictions, however GG appear to be a more robust approach for ungenotyped individuals in highly incomplete pedigrees.