Overview of genetic evaluation in dairy cattle |
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| Authors: | Kenji TOGASHI Ching Yonn LIN Kunio YOKOUCHI |
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| Institution: | National Agricultural Research Center for Hokkaido Region, Toyohira-ku, Sapporo-shi, Japan,; Department of Animal and Poultry Science, University of Guelph, Guelph, Ontario, Canada and; Incorporated Administrative Agency National Agriculture and Bio-oriented Research Organization, Tsukuba-shi, Japan |
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| Abstract: | It is costly and time‐consuming to carry out dairy cattle selection on a large experimental scale. For this reason, sire and cow evaluations are almost exclusively based on field data, which are highly affected by a large array of environmental factors. Therefore, it is crucial to adjust for those environmental effects in order to accurately estimate the genetic merits of sires and cows. Index selection is a simple extension of the ordinary least squares under the assumption that the fixed effects are assumed known without error. The mixed‐model equations (MME) of Henderson provide a simpler alternative to the generalized least squares procedure, which is computationally difficult to apply to large data sets. Solution to the MME yields the best linear unbiased estimator of the fixed effects and the best linear unbiased predictor (BLUP) of the random effects. In an animal breeding situation, the random effects such as sire or animal represent the animal's estimated breeding value, which provides a basis for selection decision. The BLUP procedure under sire model assumes random mating between sires and dams. The genetic evaluation procedure has progressed a long way from the dam‐daughter comparison method to animal model, from single trait to multiple trait analysis, and from lactational to test‐day model, to improve accuracy of evaluations. Multiple‐trait evaluation appears desirable because it takes into account the genetic and environmental variance‐covariance of all traits evaluated. For these reasons, multiple‐trait evaluation would reduce bias from selection and achieve a better accuracy of prediction as compared to single‐trait evaluation. The number of traits included in multiple‐trait evaluation should depend upon the breeding goal. Recent advances in molecular and reproductive technologies have created great potential for quantitative geneticists concerning genetic dissection of quantitative traits, and marker‐assisted genetic evaluation and selection. |
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| Keywords: | animal model marker-assisted genetic evaluation mixed model random regression model selection index |
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