Sire-Son Correlations of Genetic Evaluations for Fat Percentage in Holstein-Friesians

Ali, Tanvir; Schaeffer, L.R.

doi:10.4141/cjas87-066

Cited by 4 publications

(1 citation statement)

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“…The dairy recording system requires at least 5 testday records per flock and year for daily milk yield (DMY) of which at least 3 are sampled for fat (FC), protein (PC), and lactose content (LC), both in first and second lactation. Since 1994, the animals have been genetically evaluated by a multitrait repeatability (REP) model, fitting the lactation curve with a fixed Ali-Schaeffer regression (ASR; Ali and Schaeffer, 1987), nested within year-season of kidding (YS), regions, and lactation numbers, and with random effects of flock test day (FTD), animal, and permanent environment (Ådnøy et al, 2000). Andonov et al (2007) examined alternatives to the above model (single trait, within region, and in first lactation) and suggested the inclusion of a fixed structure with year (modeled as YS) and a description of the lactation curve by fixed effects of DIM grouped in 3-d periods (DIM3).…”

Section: Introductionmentioning

confidence: 99%

Comparison of random regression and repeatability models to predict breeding values from test-day records of Norwegian goats

Andonov

Ødegård

Svendsen

et al. 2013

Journal of Dairy Science

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One aim of the research was to challenge a previously selected repeatability model with 2 other repeatability models. The main aim, however, was to evaluate random regression models based on the repeatability model with lowest mean-squared error of prediction, using Legendre polynomials up to third order for both animal additive genetic and permanent environmental effects. The random regression and repeatability models were compared for model fit (using likelihood-ratio testing, Akaike information criterion, and the Bayesian information criterion) and the models' mean-squared errors of prediction, and by cross-validation. Cross-validation was carried out by correlating excluded observations in one data set with the animals' breeding values as predicted from the pedigree only in the remaining data, and vice versa (splitting proportion: 0.492). The data was from primiparous goats in 2 closely tied buck circles (17 flocks) in Norway, with 11,438 records for daily milk yield and 5,686 to 5,896 records for content traits (fat, protein, and lactose percentages). A simple pattern was revealed; for daily milk yield with about 5 records per animal in first lactation, a second-order random regression model should be chosen, whereas for content traits that had only about 3 observations per goat, a first-order polynomial was preferred. The likelihood-ratio test, Akaike information criterion, and mean-squared error of prediction favored more complex models, although the results from the latter and the Bayesian information criterion were in the direction of those obtained with cross-validation. As the correlation from cross-validation was largest with random regression, genetic merit was predicted more accurate with random regression models than with the repeatability model.

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Section: Introductionmentioning

confidence: 99%