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Home›Veterinary Science›Animal BLUP
Process / pipelineQuantitative Genetics

Animal BLUP

Best Linear Unbiased Predictor for Livestock Breeding · Also known as: BLUP, breeding value prediction, genetic merit estimation

Animal BLUP (Best Linear Unbiased Predictor) is a statistical method for estimating the genetic merit (breeding values) of livestock based on their own performance and the performance of their relatives. Developed by Charles R. Henderson in 1949 and refined continuously since, Animal BLUP accounts for pedigree relationships, environmental effects, and non-additive genetic variance, providing accurate predictions of an animal's ability to transmit desirable traits to offspring.

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Animal BLUP
Body Condition ScoringEquine Gait AnalysisSomatic Cell Count

When to use it

Animal BLUP is applied in organized livestock breeding programs where systematic genetic improvement is a priority. It is essential for dairy and beef cattle selection, swine and poultry breeding, and increasingly in sheep and horse breeding. BLUP is particularly valuable when genetic testing (genomic information) is limited and must be applied to traits with moderate heritability where environmental variation is substantial. Multi-trait BLUP extends the approach to correlated traits, balancing antagonistic genetic correlations (e.g., between milk yield and fertility in dairy cattle).

Strengths & limitations

Strengths
  • Accounts for pedigree relationships, using all available family information to improve prediction accuracy
  • Unbiased predictions that are optimal under the model assumptions, minimizing prediction error variance
  • Applicable to animals without own records by borrowing information from relatives, enabling early selection of young stock
  • Handles complex family structures and unbalanced data arising from real-world breeding operations
  • Foundation for genomic selection when combined with molecular markers or whole-genome data
Limitations
  • Requires accurate pedigree information; errors in parentage compromise predictions and can create bias across multiple generations
  • Assumes additive gene action and homogeneity of variance across herds or environments; violations reduce accuracy
  • Computationally intensive for large populations with complex pedigrees, requiring specialized software and expertise
  • Heritability estimates are population-specific; using incorrect values biases predictions
  • Non-additive effects (dominance, epistasis) are not modeled in traditional BLUP, potentially missing genetic variation in traits influenced by these effects

Frequently asked

What is the difference between BLUP and heritability?

Heritability is a population parameter describing the proportion of phenotypic variance due to additive genetic effects (0-1 scale). BLUP is a prediction method that uses heritability as an input to estimate an individual animal's breeding value. High heritability means BLUP predictions are more accurate; low heritability means environmental variation dominates and predictions are less precise. They are complementary concepts.

Can a young animal with no records have a BLUP?

Yes. BLUP predicts breeding values for all animals, regardless of whether they have records. A young animal's initial BLUP is based entirely on its parents' and relatives' records. As the animal accumulates its own performance data (or offspring performance), its BLUP is updated and becomes more accurate.

How does pedigree accuracy affect BLUP predictions?

Pedigree errors (e.g., incorrect parentage) bias predictions for the misidentified animal and its relatives. The bias propagates through multiple generations because BLUP relies on pedigree relationships to borrow information. Even small pedigree errors can accumulate and distort breeding decisions across many animals.

Does BLUP account for environment?

Yes, BLUP explicitly partitions observed performance into fixed environmental effects and random genetic effects. Environmental effects (e.g., herd, year, season) are estimated and removed from records before calculating breeding values. However, gene-by-environment interactions (where an animal's genetic superiority differs across environments) are not modeled in standard BLUP.

Sources

  1. Henderson, C. R. (1949). Estimation of changes in cattle breeding values. Journal of Dairy Science, 32(5), 369-378. link ↗
  2. Henderson, C. R. (1963). Selection index and expected genetic advance. Genetic Statistics and Plant Breeding, 982-993. link ↗
  3. Mrode, R. A. (2014). Linear Models for the Prediction of Animal Breeding Values (3rd ed.). CABI Publishing. link ↗

How to cite this page

ScholarGate. (2026, June 3). Best Linear Unbiased Predictor for Livestock Breeding. ScholarGate. https://scholargate.app/en/veterinary-science/animal-blup

Related methods

Body Condition ScoringEquine Gait AnalysisSomatic Cell Count

Which method?

Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.

  • Body Condition ScoringVeterinary Science↔ compare
  • Equine Gait AnalysisVeterinary Science↔ compare
  • Somatic Cell CountVeterinary Science↔ compare
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Referenced by

Body Condition ScoringSomatic Cell Count

Similar methods

Growth Curve Fitting in LivestockMilk Yield RecordingGCTAHerd Reproductive PerformanceOrdinary KrigingFeed Conversion RatioEstrus DetectionKriging

Related reference concepts

Quantitative and Heritable VariationQuantitative Genetics of EvolutionHeritability and Gene-Environment InteractionPolygenic InheritanceQuantitative Traits and Complex InheritancePopulation and Quantitative Genetics

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — Animal BLUP (Best Linear Unbiased Predictor for Livestock Breeding). Retrieved 2026-07-21 from https://scholargate.app/en/veterinary-science/animal-blup · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Charles R. Henderson
Subfamily
Quantitative Genetics
Year
1949
Type
Statistical Prediction Method
Related methods
Body Condition ScoringEquine Gait AnalysisSomatic Cell Count
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