Genetic Parameters for Functional Traits in Dairy Cattle from Daily Random Regression Models.
From: Statistical Animal Genetics Group, Institute of Animal Science, Swiss Federal Institute of Technology, ETH Centrum, Zurich CH 8092, Switzerland. burak.karacaoeren@inw.agrl.ethz.ch
Journal of dairy science
- Publish Date: Feb 2006
- ISSN: 1525-3198
- Volume: 89
- Issue: 2
- Pages: 791-8
- Medium: Internet
- Language: English
- Citation (JAMA): Karacaören B, Jaffrézic F, Kadarmideen H N, et al. Genetic Parameters for Functional Traits in Dairy Cattle from Daily Random Regression Models.. J. Dairy Sci. Feb 2006;89:791-8
Abstract
The objective of the research was to estimate genetic parameters, such as heritabilities and genetic correlations, using daily test day data for milk yield (MY), milking speed (MS), dry matter intake (DMI), and body weight (BW) using random regression methodology. Data were from first lactation dairy cows (n = 320) from the Chamau research farm of the Swiss Federal Institute of Technology, Switzerland over the period from April 1994 to 2004. All traits were recorded daily using automated machines. Estimated heritabilities (h(2)) varied from 0.18 to 0.30 (mean h(2) = 0.24) for MY, 0.003 to 0.098 (mean h(2) = 0.03) for MS, 0.22 to 0.53 (mean h(2) = 0.43) for BW, and 0.12 to 0.34 (mean h(2) = 0.23) for DMI. A permanent environmental effect was included in both the univariate and bivariate models, but was assumed constant in estimating some genetic correlations because of convergence problems. Estimated genetic correlations varied from 0.31 to 0.41 between MY and MS, from -0.47 to 0.29 between MY and DMI, from -0.60 to 0.54 between MY and BW, from 0.17 to 0.26 between MS and DMI, from -0.18 to 0.25 between MS and BW, and from -0.89 to 0.29 between DMI and BW. Genetic correlations for MY, MS, DMI, and BW from calving to midlactation decreased similarly to 0.40, 0.36, 0.14, and 0.36 and, at the end of the lactation, decreased to -0.06, 0.23, -0.07, and 0.09, respectively. Daily genetic variance-covariance of many functional traits are reported for the first time and will be useful when constructing selection indexes for more than one trait based on longitudinal genetic parameters.
Mesh Headings (Keywords): Animals, Body Weight, Cattle, Dairying, Eating, Female, Lactation, Quantitative Trait, Heritable, Regression Analysis
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