With an increase in the number of animals genotyped there has been a shift from using pedigree relationship matrices (A) to genomic ones. As the use of genomic relationship matrices (G) has increased, new methods to build or approximate G have developed. We investigated whether the way variance components are estimated should reflect these changes. We estimated variance components for maternal sow traits by solving with restricted maximum likelihood, with four methods of calculating the inverse of the relationship matrix. These methods included using just the inverse of A (A-1), combining A-1 and the direct inverse of G (HDIRECT-1), including metafounders (HMETA-1), or combining A-1 with an approximated inverse of G using the algorithm for proven and young animals (HAPY-1). There was a tendency for higher additive genetic variances and lower permanent environmental variances estimated with A-1 compared with the three H-1 methods, which supports that G-1 is better than A-1 at separating genetic and permanent environmental components, due to a better definition of the actual relationships between animals. There were limited or no differences in variance estimates between HDIRECT-1, HMETA-1, and HAPY-1. Importantly, there was limited differences in variance components, repeatability or heritability estimates between methods. Heritabilities ranged between <0.01 to 0.04 for stayability after second cycle, and farrowing rate, between 0.08 and 0.15 for litter weight variation, maximum cycle number, total number born, total number still born, and prolonged interval between weaning and first insemination, and between 0.39 and 0.44 for litter birth weight and gestation length. The limited differences in heritabilities suggest that there would be very limited changes to estimated breeding values or ranking of animals across models using the different sets of variance components. It is suggested that variance estimates continue to be made using A-1, however including G-1 is possibly more appropriate if refining the model, for traits that fit a permanent environmental effect.
- restricted maximum likelihood
- single step
- variance components