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Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattle Case

Part of the Methods in Molecular Biology book series (MIMB,volume 2467)

Abstract

In accordance with the infinitesimal model for quantitative traits, a very large number of genes affect nearly all economic traits. In only two cases has the causative polymorphism been determined for genes affecting economic traits in dairy cattle. Most current methods for genomic evaluation are based on the “two-step” method. Genetic evaluations are computed by the individual animal model, and functions of the evaluations of progeny-tested sires are the dependent variable for estimation of marker effects. With the adoption of genomic evaluation in 2008, annual rates of genetic gain in the US increased from ∼50–100% for yield traits and from threefold to fourfold for lowly heritable traits, including female fertility, herd-life and somatic cell concentration. Gradual elimination of the progeny test scheme has led to a reduction in the number of sires with daughter records and less genetic ties between years. As genotyping costs decrease, the number of cows genotyped will continue to increase, and these records will become the basic data used to compute genomic evaluations, most likely via application of “single-step” methodologies. Less emphasis in selection goals will be placed on milk production traits, and more on health, reproduction, and efficiency traits and “environmentally friendly” production. Genetic variance for economic traits is maintained by increase in frequency of rare alleles, new mutations, and changes in selection goals and management.

Key words

  • Genomic prediction
  • Genomic selection
  • Dairy cattle
  • Animal breeding
  • Complex traits

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Weller, J.I. (2022). Genomic Prediction of Complex Traits in Animal Breeding with Long Breeding History, the Dairy Cattle Case. In: Ahmadi, N., Bartholomé, J. (eds) Genomic Prediction of Complex Traits. Methods in Molecular Biology, vol 2467. Humana, New York, NY. https://doi.org/10.1007/978-1-0716-2205-6_16

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  • DOI: https://doi.org/10.1007/978-1-0716-2205-6_16

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