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- W193001752 abstract "Statistical analyses of data in animal breeding are concerned with genetic differences between 2 individuals and the proportion of variation attributable to genetic and environmental effects. 3 Often traits are recorded repeatedly per individual and may change, gradually and continually, as 4 time progresses, so that they are thought of as points on a trajectory. A typical example is growth 5 of meat producing animals, such as beef cattle. Random regression (coefficient) analyses allow for 6 variation in growth (or similar) curves for individual animals, modelled as regression on functions 7 of age, e.g. orthogonal polynomials or splines. Covariances among the regression coefficients then 8 define the covariance function between records at any pair of ages. With functions linear in the 9 coefficients to be estimated, random regression analyses are readily carried out in the linear, mixed 10 model framework commonly employed in animal breeding. Estimation of breeding values using 11 best linear unbiased prediction, and estimation of the corresponding, genetic and environmental 12 covariance components by restricted maximum likelihood using an average information algorithm 13 or Bayesian analysis using Gibbs sampling are reviewed, with emphasis on formulations based 14 on the mixed model equations. Interpretation of genetic covariance functions is discussed and 15 results from an analysis of beef cattle data are given as an example. 16" @default.
- W193001752 created "2016-06-24" @default.
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- W193001752 date "2003-01-01" @default.
- W193001752 modified "2023-09-27" @default.
- W193001752 title "Random regression models for analyses of longitudinal data in animal breeding" @default.
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