Single-Step Genomic BLUP With Unknown Parent Groups and Metafounders in Norwegian Red Evaluations.

The objective of this study was to examine the effects of different methods for handling missing pedigree data on biases, stability, relative increase in accuracy, and genetic trends using national data from Norwegian Red (NRF) cattle. The dataset comprised 8,402,773 milk yield records from 3,896,116 NRF cows, a pedigree with 4,957,544 animals, and a genomic dataset from 170,293 animals with 121,741 SNPs. Missing parents were modelled using three approaches: unknown parent groups (UPG), metafounders (MF), and "Q-Q" methods. The UPG method is routinely used for genetic evaluations of NRF cattle by including 52 fixed UPG in the pedigree. In the MF method, two MF were defined: MF14 and MF52, with MF treated as random effects. The MF14 included 6 MF defined by birth year intervals for NRF breed and 8 MF defined by breed origins for other breeds. The MF52 classification included all the 52 UPG as MF considering relationships among them. The "Q-Q" approach corrects for the combined effects of UPG and "J factor" in non-genotyped animals while avoiding such corrections in genotyped animals. The three approaches, combined with different G matrices (G matrix constructed with a 0.5 allele frequency (AF) and 10% weight (w) on A, G constructed using AF = 0.5 and w = 0.0, and G constructed with observed AF and w = 0.0), led to eight ssGBLUP models being tested. This included one UPG model (using G), four MF models (MF14 and MF52 using G or G), and three Q-Q+ models (using G, G, or G). The models were evaluated through cross-validation by masking the phenotypes of 5000 genotyped young cows. Results showed that the Q-Q models using the G or G matrix had significantly (p < 0.05) lower level biases and higher genetic trends than all other models. MF models with 14 or 52 groups using G were second best for level bias and performed similarly or slightly better than Q-Q+ models regarding inflation bias and stability. Increasing the number of MF from 14 to 52 had minimal effects on biases but significantly improved stability and genetic trend estimates. Models with G had slightly higher gain in accuracy from adding phenotypic data (2.01%) than G (1.18%), but pedigree-based models showed the highest improvement in accuracy due to adding phenotypic (26%) or genomic (47%) data to the partial dataset. Overall, all models with G showed the least bias (with a small standard error) and most stable predictions, while models using G introduced biases and instability. Thus, the Q-Q and MF models combined with G and Q-Q with G are recommended for their improved validation results and genetic trends.