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Journal of Dairy Science
- Source :
- Journal of Dairy Science, 100(3), 2007-2016, Journal of Dairy Science 100 (2017) 3
- Publication Year :
- 2016
-
Abstract
- Feed efficiency in dairy cattle has gained much attention recently. Due to the cost-prohibitive measurement of individual feed intakes, combining data from multiple countries is often necessary to ensure an adequate reference population. It may then be essential to model genetic heterogeneity when making inferences about feed efficiency or selecting efficient cattle using genomic information. In this study, we constructed a marker x environment interaction model that decomposed marker effects into main effects and interaction components that were specific to each environment. We compared environment-specific variance component estimates and prediction accuracies from the interaction model analyses, an across-environment analyses ignoring population stratification, and a within-environment analyses using an international feed efficiency data set. Phenotypes included residual feed intake, dry matter intake, net energy in milk, and metabolic body weight from 3,656 cows measured in 3 broadly defined environments: North America (NAM), the Netherlands (NLD), and Scotland (SAC). Genotypic data included 57,574 single nucleotide polymorphisms per animal. The interaction model gave the highest prediction accuracy for metabolic body weight, which had the largest estimated heritabilities ranging from 0.37 to 0.55. The within environment model performed the best when predicting residual feed intake, which had the lowest estimated heritabilities ranging from 0.13 to 0.41. For traits (dry matter intake and net energy in milk) with intermediate estimated heritabilities (0.21 to 0.50 and 0.17 to 0.53, respectively), performance of the 3 models was comparable. Genomic correlations between environments also were computed using variance component estimates from the interaction model. Averaged across all traits, genomic correlations were highest between NAM and NLD, and lowest between NAM and SAC. In conclusion, the interaction model provided a novel way to evaluate traits measured in multiple environments in which genetic heterogeneity may exist. This model allowed estimation of environment-specific parameters and provided genomic predictions that approached or exceeded the accuracy of competing within- or across environment models. Agriculture and Food Research Initiative Competitive Grants from the USDA National Institute of Food and Agriculture (Washington, DC) [2008-35205-18711, 2011-68004-30340]; Hatch grant from the Wisconsin Agricultural Experiment Station (Madison, WI) [MSN139239]; National Association of Animal Breeders (Columbia, MO) This project was supported by Agriculture and Food Research Initiative Competitive Grants no. 2008-35205-18711 and 2011-68004-30340 from the USDA National Institute of Food and Agriculture (Washington, DC). Support from Hatch grant no. MSN139239 from the Wisconsin Agricultural Experiment Station (Madison, WI) is acknowledged, and K. A. Weigel acknowledges partial financial support from the National Association of Animal Breeders (Columbia, MO). Public domain – authored by a U.S. government employee
- Subjects :
- 0301 basic medicine
Genotype
Feed efficiency
Net energy
Biology
Population stratification
Feed conversion ratio
03 medical and health sciences
Eating
Genetic Heterogeneity
Animal science
Genetics
Animals
Lactation
Interaction model
Dry matter
Fokkerij & Genomica
Gene–environment interaction
Dairy cattle
Genomic selection
business.industry
Genetic heterogeneity
Body Weight
0402 animal and dairy science
04 agricultural and veterinary sciences
040201 dairy & animal science
Biotechnology
030104 developmental biology
Milk
WIAS
Animal Science and Zoology
Cattle
Female
Gene-Environment Interaction
Residual feed intake
business
Animal Breeding & Genomics
Food Science
Subjects
Details
- ISSN :
- 15253198 and 00220302
- Volume :
- 100
- Issue :
- 3
- Database :
- OpenAIRE
- Journal :
- Journal of dairy science
- Accession number :
- edsair.doi.dedup.....56c86a45a2e743d13a721c99a4a1dc38