Abstract

This study compared the abilities of virgin heifer genomically enhanced breeding values (GEBV) and parent average breeding values (PA) to predict future cow performance. To increase confidence in genomic technology among farmers, a clear demonstration of the relationship between genomic predictions and future phenotypes is needed. We analyzed 12 different traits in first parity, including production, conformation, fer-tility, and other functional traits. Phenotype data were obtained from national milk recording schemes and breeding values from the Nordic Cattle Genetic Evalu-ation. Direct genomic breeding values were calculated using genomic BLUP and combined with traditional breeding values, using bivariate blending. The data covered 14,862 Red Dairy Cattle, 17,145 Holstein, and 7,330 Jersey genotyped virgin heifers born between 2013 and 2015 in Denmark, Finland, and Sweden. Phe-notypes adjusted for systematic environmental effects were used as measures of cow performance. Two meth-ods were used to compared virgin heifer GEBV and PA regarding their ability to predict future cow per-formance: (1) correlations between breeding values and adjusted phenotypes, (2) ranking cows into 4 quartiles for their virgin heifer GEBV or PA, and calculating actual cow performance for each quartile. We showed that virgin heifer GEBV predicted cow performance significantly better than PA for the vast majority of analyzed traits. The correlations with adjusted pheno-types were 38 to 136% higher for GEBV than for PA in Red Dairy Cattle, 42 to 194% higher for GEBV in Holstein, and 11 to 78% higher for GEBV in Jersey. The relative change between GEBV bottom and top quartiles compared with that between PA bottom and top quartiles ranged from 9 to 261% for RDC, 42 to 138% for Holstein, and 4 to 90% for Jersey. Hence, farmers in Denmark, Finland, and Sweden can have confidence in using genomic technology on their herds.

Keywords

genomic breeding value; genotyping; dairy cow; validation

Published in

Journal of Dairy Science
2020, volume: 103, number: 7, pages: 6383-6391
Publisher: ELSEVIER SCIENCE INC

Authors' information