Abstract
Single-step GBLUP (ssGBLUP) relies on the combination of genomic (G) and pedigree relationships for all (A) and genotyped animals (A22). The procedure implemented in the BLUPF90 software suite first involves combining a small percentage of A22 into G (blending) to avoid singularity problems, then an adjustment to account for the fact the genetic base in G and A22 is different (tuning). However, blending before tuning may not reflect the actual difference between pedigree and genomic base because the blended matrix already contains a portion of A22. The objective of this study was to evaluate the impact of tuning before blending on predictivity, bias, and inflation of GEBV, indirect predictions (IP), and SNP effects from ssGBLUP using American Angus and US Holstein data. We used four different scenarios to obtain genomic predictions: BlendFirst_TunedG2, TuneFirst_TunedG2, BlendFirst_TunedG4, and TuneFirst_TunedG4. TunedG2 adjusts mean diagonals and off-diagonals of G to be similar to the ones in A22, whereas TunedG4 adjusts based on the fixation index. Over 6 million growth records were available for Angus and 5.9 million udder depth records for Holsteins. Genomic information was available on 51,478 Angus and 105,116 Holstein animals. Predictivity and reliability were obtained for 19,056 and 1,711 validation Angus and Holsteins, respectively. We observed the same predictivity and reliability for GEBV or IP in all four scenarios, ranging from 0.47 to 0.60 for Angus and was 0.67 for Holsteins. Slightly less bias was observed when tuning was done before blending. Correlation of SNP effects between scenarios was > 0.99. Refined tuning before blending had no impact on GEBV and marginally reduced the bias. This option will be implemented in the BLUPF90 software suite.
Reliability of Genomic Predictions Across Multiple Populations
