142 Genotype-By-Environment Interactions in Dairy Cattle Raised in California and New England

• Published in: Journal of animal science 2023-11, Vol.101 (Supplement_3), p.37-38
• Main Authors: Santana, Bruna, Miles, Asha, Tiezzi, Francesco, Fragomeni, Breno O
• Format: Article
• Language: English
• Subjects: Animal breeding; Animal husbandry; Animals; Breeding; Cattle; Correlation coefficient; Correlation coefficients; Dairy cattle; Decision making; Environmental effects; Environmental factors; Genetic effects; Genome-wide association studies; Genomics; Genotype & phenotype; Genotype-environment interactions; Genotypes; Heritability; Milk; Parity; Phenotypes; Variance
• ISSN: 0021-8812; 1525-3163
• DOI: 10.1093/jas/skad281.046

Abstract Genotype-by-environmental interactions (G x E) have been reported to cause sire reranking in dairy populations. However, the effects of such interactions are not included in the current US national dairy genetic evaluation. Phenotypes collected from animals across the country are considered the same trait, disregarding interactions between genetic effects and region or other environmental factors. Variations in production systems may interact with the genotypes of the animals, leading to distinct phenotypes. Separating data into regions could change decision-making on bull selection, which is the most important step of a breeding program. The goal of this study is to evaluate the extent of genotype-by-environment interaction between the state of California and the region of New England in the US Holstein Population. Data from Holstein cows in the aforementioned regions collected from 2010 to 2020 were provided by the Council on Dairy Cattle Breeding. The genetic evaluation models used for the national evaluation were modified to account for New England and California phenotypes as separated yet correlated traits. The mixed model accounted for additive genetic, sire-by-herd interaction, and permanent environmental effects as random, and for the management group, age of parity, birth year, and parity as fixed effects. In some scenarios, sire-by-herd interaction and permanent environment were removed due to convergence problems. Variance components were estimated using AIREML under the Method R procedure. Such a method allows computing variance components for large databases by splitting the data into several random groups. Ten random samples of data containing 100,000 records were used for each analysis. The scenarios included single trait for each state, single trait with both states, and two-trait with separated states. Breeding values from the top 100 bulls in each region were compared with evaluate potential sire reranking. Heritabilities varied across regions for all three traits (Table 1). California presented lower heritability than New England across methods. The genetic correlations on the two-trait analysis averaged at 0.96, 0.92, and 0.95 for yield, fat, and protein, respectively. The Spearman correlation coefficient between breeding values of the bulls with more than 200 daughters was 0.87 for milk yield, suggesting that the reranking is unimportant. The genetic correlations suggested that GxE between the two regions is small and that th