- Best Linear Unbiased Prediction (BLUP) is one of the most important statistical methods used in animal breeding and quantitative genetics to estimate the genetic merit of animals. It helps breeders identify animals that are likely to pass desirable traits to their offspring, supporting genetic improvement in livestock populations. BLUP is widely used in dairy cattle, beef cattle, sheep, goats, pigs, poultry, and other domesticated animals. It has become a fundamental part of modern genetic evaluation because it combines performance records, pedigree information, and statistical models to make more reliable predictions of breeding values.
- The main purpose of BLUP is to predict the genetic value of an animal while accounting for environmental factors that influence its observed performance. Traits such as milk yield, body weight, growth rate, fertility, feed efficiency, and disease resistance are influenced by both genetic and environmental conditions. For example, a dairy cow may produce more milk because it has superior genetic potential, but its production may also be increased by high-quality feed, good housing, and effective health management. If these environmental differences are ignored, breeders may incorrectly select animals that perform well primarily because they receive better care. BLUP helps adjust for such differences, making genetic comparisons fairer and more informative.
- The term Best Linear Unbiased Prediction describes three important properties of the method. “Best” means that, under the specified statistical model and its assumptions, BLUP provides predictions with minimum prediction error variance among linear unbiased predictors. “Linear” means that the predictions are linear functions of the observed data. “Unbiased” means that the prediction procedure has the appropriate expectation under the model, while “prediction” refers to estimating random effects, such as the genetic merit of an animal. These properties make BLUP especially useful when researchers need to predict breeding values from large datasets containing animals with different numbers of records and different levels of genetic relatedness.
- BLUP is commonly applied through mixed linear models, which include both fixed and random effects. A general mixed model can be written as y=Xb+Zu+e, where y represents the observed performance records, X is the design matrix for fixed effects, b represents fixed-effect solutions, Z is the design matrix for random effects, u represents random effects, and e represents residual errors. Fixed effects may include herd, year, season, sex, age, and management group. Random effects often include the additive genetic effects of animals and, where appropriate, permanent environmental effects. This framework allows BLUP to separate systematic environmental influences from the genetic differences that breeders want to evaluate.
- In animal breeding, the random genetic effect predicted by BLUP is commonly expressed as an estimated breeding value (EBV). A breeding value represents the additive genetic merit of an animal for a particular trait and predicts the average genetic contribution it can pass to its offspring. For example, a bull with a high breeding value for growth rate is expected, on average, to transmit favorable growth-related alleles to its progeny compared with a bull with a lower breeding value, assuming the evaluations are comparable. An EBV is not a guarantee of an animal’s future performance or the exact performance of its offspring. Rather, it is a statistical prediction based on available information and the assumptions of the genetic evaluation model.
- One of the major strengths of BLUP is its ability to use information from relatives. Animals inherit genes from their parents and share portions of their genomes with siblings, offspring, and other relatives. Consequently, the performance records of related animals can provide useful information about an animal’s genetic merit. An animal with limited individual records may still receive a useful breeding value if its relatives have informative records. For instance, the genetic merit of a young bull can be predicted using performance records from its parents, siblings, and other relatives. As additional information becomes available, its breeding value can be updated.
- The animal model is one of the most widely used applications of BLUP in livestock genetic evaluation. In an animal model, the genetic effect of each animal is included in the model, and the relationships among animals are represented using a numerator relationship matrix derived from pedigree information. This matrix describes the expected genetic relationships among animals based on their recorded ancestry. The model therefore uses information from the animal itself and its relatives while accounting for differences in the amount of information available for each individual. Pedigree-based animal models have been extensively used to estimate breeding values for milk production, growth, reproductive performance, and many other traits.
- BLUP also supports genetic evaluation when animals are raised in different herds, farms, years, or management systems. Livestock performance is affected by the environment in which animals are raised, and these environmental influences can make direct comparisons misleading. For example, animals raised in a well-managed herd may grow faster than animals in a herd with limited feed, even when their genetic potential is similar. By including relevant environmental effects in the model, BLUP adjusts observed records to account for systematic differences. When herds or groups are genetically connected through shared sires or other relationships, the evaluation can make more meaningful comparisons across groups. The quality of these comparisons depends on the accuracy of the data and the strength of the genetic connections between groups.
- Another important feature of BLUP is its handling of unequal amounts of information. In practical breeding populations, some animals have many performance records, while others have only one or none. Some animals have many recorded relatives, whereas others have little pedigree information. BLUP accounts for these differences through the statistical model and the estimated variance components. Predictions are influenced by how much reliable information is available, so animals with limited information generally have less precise breeding values than animals supported by extensive records. This helps breeders assess both the predicted genetic merit and the reliability of that prediction.
- The accuracy of breeding value prediction depends on several factors, including the heritability of the trait, the number and quality of performance records, the number of informative relatives, the completeness of pedigree information, and the suitability of the statistical model. Traits with higher heritability often allow more accurate prediction from an animal’s own performance, while traits with lower heritability may require more records from relatives or progeny. Heritability describes the proportion of phenotypic variance attributable to additive genetic variance in a particular population and environment. It does not mean that a fixed percentage of an individual animal’s trait is genetically determined.
- BLUP has been especially influential in dairy cattle breeding, where genetic evaluations combine large numbers of milk production records and pedigree relationships. Traits such as milk yield, milk fat, protein content, fertility, udder health, and longevity can be evaluated using suitable statistical models. Repeated lactation records provide additional information about the performance of cows over time. By adjusting for systematic environmental effects and accounting for relationships among animals, BLUP helps breeders identify parents that are likely to contribute to long-term genetic improvement. Similar principles are applied in beef cattle, where growth, carcass characteristics, maternal ability, and feed efficiency are important selection traits.
- In sheep, goats, pigs, and poultry, BLUP is used to evaluate traits relevant to productivity, reproduction, and product quality. In pigs, for example, breeders may use BLUP to predict breeding values for daily gain, feed conversion, litter size, and carcass quality. In sheep, evaluations may focus on growth, wool production, reproductive performance, and parasite resistance. In poultry, breeding programs can use related methods to evaluate egg production, growth, feed efficiency, fertility, and survival. The exact statistical model differs according to the trait, species, breeding structure, and available records, but the general objective remains the same: to estimate genetic merit as accurately as possible while accounting for relevant sources of variation.
- BLUP is also important for traits that are difficult or expensive to measure directly. Some traits, such as disease resistance, carcass quality, or longevity, may require extensive data collection or may only be observed late in an animal’s life. In such cases, information from relatives can improve early genetic evaluations. For example, the breeding value of a young animal may be predicted from the records of older relatives that have already expressed the trait. Progeny testing has historically been an important way to evaluate breeding animals, particularly dairy bulls, because the performance of many offspring can provide information about a sire’s genetic merit. Although genomic selection has changed how early evaluations are conducted, BLUP-based methods remain central to many genetic evaluation systems.
- The integration of genomic information has expanded the applications of BLUP. Genomic best linear unbiased prediction (GBLUP) uses genomic relationships derived from DNA marker data rather than relying exclusively on pedigree relationships. Genomic relationships can provide a more detailed representation of the genetic similarity among animals and may improve prediction accuracy, particularly when genomic information is available for the animals being evaluated. Single-step genomic BLUP goes further by combining pedigree, genomic, and phenotypic information in a unified evaluation framework. This makes it possible to evaluate genotyped and non-genotyped animals together, provided the model and data are appropriately constructed.
- It is important to distinguish BLUP from the methods used to estimate the model’s variance components. BLUP predicts random effects, such as breeding values, assuming the relevant model parameters are known. In practice, the genetic and residual variances are often unknown and must be estimated from the available data, using methods such as restricted maximum likelihood (REML). These estimates are then used in the genetic evaluation process. The quality of the resulting breeding values depends on the adequacy of the model, the reliability of the variance estimates, and the quality of the data. An incorrect model or poorly recorded pedigree can reduce the usefulness of the predictions.
- Although BLUP offers many advantages, it has limitations. Its predictions depend on the assumptions of the statistical model, including the assumed distributions and covariance structures of the random effects. Important environmental effects that are omitted from the model may distort the results. Inaccurate animal identification, incorrect pedigree information, inconsistent measurement procedures, and poor-quality records can also reduce prediction accuracy. Furthermore, breeding values are usually most meaningful within the population and evaluation system in which they are calculated. Comparing EBVs from different countries, breeds, or evaluation programs may require adjustments to account for differences in genetic bases, trait definitions, and statistical procedures.
- BLUP should also be understood as a method for prediction rather than a method that automatically identifies causal genes. It estimates genetic merit from patterns in the data and the relationships specified by the model. Its usefulness depends on sound breeding objectives, reliable measurements, and appropriate interpretation of the results. Breeders should consider the economic and biological importance of each trait, the reliability of the estimated breeding values, and the potential consequences of selection for correlated traits. Selecting animals exclusively for a single trait may produce undesirable changes in other characteristics, so practical breeding programs often use selection indexes that combine several breeding values according to their importance.
- Modern statistical software has made BLUP increasingly accessible for large-scale genetic evaluation. Specialized animal breeding programs and general statistical packages can fit mixed models, estimate variance components, and predict breeding values using pedigree and genomic information. Nevertheless, software alone cannot ensure valid results. Geneticists must select appropriate models, verify data quality, assess prediction reliability, and explain the results in a form that breeders can use in practice. Effective communication between researchers and livestock producers is essential for translating statistical predictions into real genetic progress.
- Best Linear Unbiased Prediction has transformed animal breeding by providing a systematic way to compare animals using information from their performance, relatives, and production environments. Its ability to account for fixed effects, genetic relationships, and unequal amounts of information makes it a powerful method for estimating breeding values in complex livestock populations. With the continued integration of genomic data, repeated measurements, and improved statistical models, BLUP remains a central tool in modern quantitative genetics. Its application helps breeders make more informed selection decisions and improve productivity, animal health, reproductive performance, and the long-term sustainability of livestock production.
