Genomic Selection in Dairy Cattle: Principles, Implementation, and Future Perspectives

Abstract

Genomic selection (GS) has fundamentally transformed dairy cattle breeding by enabling highly accurate prediction of breeding values in young animals long before progeny testing data become available. First proposed theoretically by Meuwissen et al (2001) and implemented commercially in 2008-2009 in the United States and Western Europe, GS exploits the linkage disequilibrium between dense single nucleotide polymorphism (SNP) markers distributed across the genome and quantitative trait loci to estimate genomic estimated breeding values (GEBVs). This review comprehensively examines the theoretical underpinnings of GS, the statistical models employed (GBLUP, ssGBLUP, Bayesian alphabet methods), the SNP genotyping platforms currently in use, the critical role of reference population size and composition, the impact of imputation on cost reduction, across- country genomic evaluations coordinated through Interbull, and the economic consequences of adopting GS in place of progeny testing. Empirical data from Holstein, Jersey, Brown Swiss, and several Nordic breeds confirm that GS has approximately doubled the rate of genetic gain per year while simultaneously reducing progeny testing costs by over 90%. The review also addresses current limitations — including reduced accuracy for low- heritability and female traits, limited performance for minor breeds with small reference populations, and the challenge of accounting for genotype- by- environment interaction and proposes research priorities for the next decade, including whole- genome sequence- based selection, functional annotation of genomic regions through the FAANG initiative, and development of low- cost genotyping solutions for developing- country dairy systems.