What question did this study set out to answer?

To assess the effectiveness of alternative ssGBLUP models for genomic predictions of milk production traits in Holstein cattle.

January 25, 2026AnimalsOpen Access

Evaluating Adjusted ssGBLUP Models for Genomic Prediction and Matrix Compatibility in South African Holstein Cattle

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Evaluates genomic predictions in South African Holstein cattle, suggesting improved accuracy with adjusted ssGBLUP models.

To assess the effectiveness of alternative ssGBLUP models for genomic predictions of milk production traits in Holstein cattle.
Analyzed five different ssGBLUP models for genomic predictions.
Utilized a dataset of 696,413 milk production records and 541,325 pedigree records.
Incorporated genotype data from the Illumina 50K chip v3 with 53,218 SNPs.
Measured genomic estimated breeding values (GEBVs) for milk, fat, and protein yields.
The ssGBLUP_adjusted models showed a 6-7% improvement in accuracy compared to standard ssGBLUP.
GEBV accuracy for milk, protein, and fat yields ranged from 0.23 to 0.37 for various models.
Bias in predictions decreased significantly with ssGBLUP_adjusted models, especially for milk and protein yields.

Mafolo et al. (2026) studied this question.

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