Single-step genomic BLUP (ssGBLUP) effectively models small cattle populations: lessons from the Israeli-Holstein Herdbook

Abstract

Abstract Background Routine genomic-estimated breeding values (gEBVs) are computed for the Israeli dairy cattle population by a two-step methodology in combination with the much larger Dutch population. Only sire genotypes are included. This work evaluated the contribution of cow genotypes obtained from the Israeli Holstein population to enhance gEBVs predictions via single-step genomic best-linear unbiased prediction (ssGBLUP). The gEBV values of 141 bulls with daughter information and high reliabilities for 305-day lactation yield of milk, fat, and protein were compared with the bulls’ predicted ssGBLUP-gEBVs using a truncated dataset omitting production data of the last five years. We investigated how these sire gEBVs were affected by varying polygenic weights in the genomic relationship matrices and by deleting old phenotypic or genotypic records. Results The correlations of the predicted gEBVs for milk, fat and protein computed from the truncated data with the current gEBVs based also on daughter records of the last five years were 0.64, 0.57, and 0.56, respectively, for a polygenic weight of 0.5, similar to the values achieved by the current two-step methodology. The regressions of the current gEBVs on the predicted values were 0.9 for milk and 0.7 for fat and protein. Genotyping of 1.8-5 cows had the approximate statistical power of one additional bull depending on the trait. Omitting phenotype records earlier than 2000 resulted in similar gEBV values. Omitting genotypes before 1995 improved the regression coefficients. For all experiments, varying the polygenic weights over the range of 0.1 to 0.9 resulted in a trade-off between correlations and overestimation of gEBVs for young bulls. Conclusions The model suffers from overestimation of the predicted values for young bulls. The time interval used for inclusion of genotypic and phenotypic records and adjustment of the polygenic weight can improve gEBV predictions and should be tuned to fit the tested population. For relatively small populations, genotyping of cows can significantly increase the reliability of gEBVs computed by single-step methodology. By extrapolation of our results, records of ~ 13,000 genotyped cows should provide a sufficiently large training population to obtain reliable estimates of gEBVs using ssGBLUP.

Keywords

• APEX- Linear mixed Model Software
• Dairy cattle
• Genomic evaluation
• Israeli holsteins
• Inflation