Genetic Improvement Strategies in Dairy Cattle Using Genomic Selection

Abstract

Genomic selection (GS) has fundamentally transformed dairy cattle breeding by enabling accurate prediction of breeding
values for economically important traits using high-density SNP marker arrays, bypassing the lengthy progeny-testing
cycle that characterised conventional pedigree-based selection. This study evaluates genomic estimated breeding value
(GEBV) prediction accuracy for six key dairy traits--milk yield, fat percentage, protein percentage, somatic cell score
(SCS), fertility index, and longevity--across three breeds (Holstein-Friesian, Jersey, and Brown Swiss) using a reference
population of 12,847 genotyped animals from Sweden, France, and Austria. Three prediction models were compared:
genomic BLUP (GBLUP), Bayesian Ridge Regression (BRR), and a Gradient Boosting Machine (GBM) integrating
genomic and phenotypic covariates. GBLUP achieved the highest cross-validated prediction accuracy for milk yield
(r=0.78) and protein percentage (r=0.81), while GBM outperformed both for fertility index (r=0.71) and longevity (r=0.69)
where non-additive genetic effects and genotype-by-environment interactions are substantial. Genomic selection
reduced the generation interval from 6.2 years (conventional) to 2.1 years, translating to an estimated 47% increase in
annual genetic gain for milk protein yield. Multi-trait genomic index optimisation incorporating economic weights for all six
traits demonstrated the potential to increase net merit by 18.4% above single-trait selection scenarios.
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