- Accuracy and reliability are two important concepts in animal breeding and quantitative genetics because they determine how confidently breeders can identify animals with superior genetic potential. Genetic evaluation is the process of estimating the genetic merit of animals using information such as performance records, pedigree relationships, progeny performance, and genomic data. The results of genetic evaluation help breeders select animals for reproduction and improve economically important traits, including milk production, growth rate, fertility, disease resistance, feed efficiency, meat quality, and longevity. However, genetic evaluations are based on statistical predictions rather than direct measurements of an animal’s complete genetic potential. Therefore, understanding the accuracy and reliability of these predictions is essential for making effective breeding decisions.
- Accuracy of genetic evaluation refers to the strength of the relationship between an estimated breeding value and the animal’s true breeding value. The true breeding value represents the animal’s actual additive genetic contribution to the performance of its offspring, although it cannot normally be measured directly. An estimated breeding value (EBV) is calculated using available information and statistical methods to predict this genetic contribution. The accuracy of an EBV indicates how closely the estimate is expected to reflect the true breeding value. Accuracy is usually expressed as a value between zero and one. A value close to one indicates a strong relationship between the estimated and true breeding values, while a value close to zero indicates substantial uncertainty in the estimate. In practical animal breeding, highly accurate evaluations help breeders make more confident selection decisions.
- Reliability is closely related to accuracy and describes the proportion of the uncertainty in an estimated breeding value that has been reduced through available information. It is commonly expressed as a percentage between zero and one hundred. Reliability is calculated as the square of the accuracy when accuracy is expressed on a zero-to-one scale. For example, if the accuracy of an estimated breeding value is 0.80, its reliability is 0.64, or 64%. This means that the evaluation has reduced a substantial portion of the uncertainty associated with the prediction, but uncertainty remains. Accuracy and reliability therefore describe related aspects of genetic evaluation, but they are not interchangeable numerical measures. Accuracy is expressed as a correlation, whereas reliability is expressed as its square.
- The accuracy of genetic evaluation depends on several factors, including the quantity and quality of available information, the heritability of the trait, the number of relatives with records, the genetic relationships among animals, and the statistical model used for evaluation. Animals with many informative records from themselves and their relatives can often receive more accurate breeding value estimates than animals with limited information. Nevertheless, the amount of data alone does not guarantee high accuracy. Records must be relevant to the trait being evaluated, correctly recorded, and appropriately adjusted for environmental and management differences. Errors in identification, pedigree information, performance measurement, or data processing can reduce the quality of genetic evaluations and lead to misleading selection decisions.
- Heritability is particularly important because it describes the proportion of observed variation in a population that is attributable to additive genetic differences under specific environmental conditions. Traits with higher heritability generally allow more accurate genetic evaluation from an individual’s own performance record, provided that the record is informative and the evaluation model is appropriate. For example, body weight at a specified age may provide useful information about genetic differences in growth, whereas fertility traits often have lower heritability and are more strongly influenced by environmental and management factors. Consequently, evaluating fertility may require records from many relatives, repeated observations, or information collected across several reproductive cycles. However, heritability alone does not determine accuracy because the amount of information, population structure, trait definition, and statistical methodology also play important roles.
- Pedigree information contributes to the accuracy of genetic evaluation by describing relationships among animals. An animal inherits approximately half of its additive genetic material from each parent, making parental breeding values useful sources of information about its expected genetic merit. Information from grandparents, siblings, half-siblings, and other relatives can also improve the evaluation. For example, a young bull with no daughters in production may still receive an estimated breeding value based on its pedigree, its own performance where relevant, and the records of genetically related animals. However, pedigree-based estimates depend on the completeness and correctness of the pedigree and on the amount of useful information available from relatives. Animals with incomplete pedigrees or few recorded relatives may have less accurate evaluations.
- Performance records provide direct evidence about how animals express particular traits under their production environments. In dairy cattle, milk yield, fat percentage, protein percentage, fertility, and somatic cell count may contribute to genetic evaluation. In beef cattle, growth rate, carcass characteristics, calving performance, and feed efficiency may be important. In sheep and goats, relevant records may include litter size, lamb or kid growth, milk production, and parasite resistance. In poultry, egg production, egg quality, feed conversion, and survival may be evaluated. The usefulness of these records depends on the trait, the age at measurement, the recording system, and the degree to which environmental effects are accounted for. A single record may provide limited evidence, while repeated or related records can strengthen the evaluation when they contribute additional independent information.
- Environmental effects are a major challenge in animal breeding because observed performance reflects both genetic and non-genetic influences. Factors such as nutrition, housing, climate, disease exposure, age, sex, management practices, and herd or flock conditions can affect performance. If these influences are not adequately accounted for, animals raised under favorable conditions may appear genetically superior even when some of their observed advantage is environmental. Statistical models such as animal models and mixed models help separate genetic effects from environmental influences by incorporating appropriate fixed and random effects. Best Linear Unbiased Prediction (BLUP) is widely used to estimate breeding values while accounting for systematic environmental differences and relationships among animals. The effectiveness of BLUP depends on the suitability of the model, the quality of the data, and the assumptions underlying the analysis.
- Genomic information has substantially changed the accuracy and timing of genetic evaluation in many livestock populations. Genomic evaluation uses information from genetic markers distributed throughout the genome to estimate the genetic merit of animals. Genomic estimated breeding values can be particularly useful for young animals that have not yet produced offspring or accumulated extensive performance records. By using genetic relationships estimated from DNA information, genomic methods can improve the ranking of animals for selection, especially when a suitable reference population contains animals with both genomic data and reliable phenotypic or breeding value information. The benefits depend on the size and genetic composition of the reference population, the quality of genotyping, the trait being evaluated, and the genetic similarity between reference animals and the candidates being assessed. Genomic evaluations may become less accurate when applied to populations that differ substantially from the population used to develop the prediction equations.
- Progeny testing is another important method for improving the accuracy of genetic evaluation, particularly for traits that cannot be measured directly in breeding males or that require information from offspring. In dairy cattle, for example, a bull’s breeding value for milk production can be estimated using the performance of its daughters. The number of offspring with useful records, the quality of those records, and the environments in which they are raised influence the amount of information available. Progeny testing can provide strong evidence about genetic merit, but it may require considerable time and financial investment. Genomic selection can reduce reliance on extensive progeny testing for some traits by allowing selection at a younger age. In many breeding programs, combining genomic information, pedigree records, and performance data produces more informative evaluations than relying on a single source of information.
- The statistical methods used for genetic evaluation also influence accuracy and reliability. BLUP, animal models, sire models, maternal models, random regression models, and threshold models are designed for different data structures and biological traits. Animal models use relationships among animals to estimate breeding values and can incorporate records from individuals and their relatives. Maternal models help distinguish the genetic contribution of the offspring from maternal genetic and environmental effects. Random regression models are useful for traits measured repeatedly over time, such as milk production throughout lactation or body weight during growth. Threshold models are suitable for categorical traits that reflect an underlying continuous liability, such as some disease and reproductive outcomes. Choosing a model that reflects the biological characteristics of the trait and the structure of the data can improve the quality of genetic predictions.
- Accuracy and reliability are especially important when comparing animals for selection. Two animals may have similar estimated breeding values but different reliability levels. One animal may have an estimate supported by extensive records, while the other may have an estimate based on limited pedigree or genomic information. The first estimate is generally more dependable, whereas the second may change more as additional information becomes available. Breeders should therefore consider both the estimated breeding value and its reliability rather than selecting animals solely on the numerical ranking of their estimates. However, a lower reliability does not necessarily mean that an animal has poor genetic merit; it means that greater uncertainty surrounds the current estimate.
- The reliability of genetic evaluation also affects the expected response to selection. Genetic improvement depends on factors such as selection intensity, additive genetic variation, and the generation interval. More accurate breeding values generally improve the effectiveness of selection because they help breeders identify animals whose genetic merit is closer to the desired breeding objective. When accuracy is low, selection decisions may be influenced more strongly by prediction errors, increasing the possibility that genetically inferior animals are chosen over superior ones. Increasing accuracy can therefore improve long-term genetic progress, although the cost and time required to obtain additional information must also be considered. Breeding programs need to balance the benefits of more accurate evaluation against expenses associated with recording, genotyping, progeny testing, and data management.
- Genetic evaluation should also account for the possibility that animals perform differently across environments. This phenomenon is known as genotype-by-environment interaction. For example, animals selected for high production under intensive management may not maintain the same advantage under hot climates, limited feeding conditions, or extensive production systems. If genetic rankings change across environments, a single evaluation may not fully describe an animal’s suitability for every production system. Breeders may need environment-specific evaluations or models that account for genetic differences in response to environmental conditions. This is especially important in regions experiencing climate change, where heat stress, changes in feed availability, and disease pressure may alter the relative performance of different genotypes.
- Another important consideration is the difference between accuracy of prediction and the actual future performance of an animal’s offspring. Even a highly accurate estimated breeding value does not guarantee that every offspring will perform at a particular level. Offspring inherit a random sample of parental genes, and their performance is also influenced by the genes inherited from the other parent, environmental conditions, and random biological variation. Genetic evaluation predicts expected genetic merit at the population level; it does not remove the natural variation among individuals. Breeders should interpret evaluations as tools for improving the probability of favorable outcomes rather than as guarantees of specific results.
- Reliability can also change over time as new information becomes available. A young animal may initially have a relatively low-reliability estimate based on pedigree or genomic data. As its own performance records, progeny information, or additional relatives’ records become available, the estimate may become more reliable. The estimated breeding value itself may increase or decrease as the evidence changes. This is a normal feature of genetic evaluation rather than necessarily a sign of failure. Modern genetic evaluation systems are designed to update estimates as new data are added, allowing breeding decisions to reflect the best available information.
- In practical breeding programs, accuracy and reliability should be evaluated together with economic importance, genetic diversity, inbreeding, and animal welfare. Selecting only the animals with the highest estimated breeding values for a single trait may produce undesirable changes in other characteristics. For example, strong selection for production without sufficient attention to fertility, health, longevity, or structural soundness can create long-term problems. Selection indexes combine multiple traits according to their genetic relationships and economic importance, helping breeders balance competing objectives. Maintaining genetic diversity is also essential because excessive reliance on a small number of highly ranked animals can increase inbreeding and reduce future opportunities for genetic improvement.
- Improving the accuracy of genetic evaluation requires consistent and reliable data collection, accurate animal identification, complete pedigree recording, appropriate statistical models, and effective use of genomic information. Breeding organizations should monitor data quality, validate prediction methods, update reference populations, and assess the performance of evaluations across generations and production environments. International and national genetic evaluation systems may also need to account for differences in breeds, management practices, trait definitions, and recording procedures. Transparent reporting of estimated breeding values, accuracy or reliability, and the population to which the evaluation applies helps breeders interpret results correctly and make informed decisions.
- In conclusion, accuracy and reliability are fundamental measures of the quality of genetic evaluation in animal breeding. Accuracy describes the relationship between an estimated breeding value and the true breeding value, while reliability expresses the proportion of uncertainty accounted for by the evaluation. Both are influenced by heritability, data quantity and quality, pedigree relationships, progeny records, genomic information, environmental adjustments, and the statistical models used. High-quality genetic evaluation helps breeders select animals more effectively, improve economically important traits, and achieve sustainable genetic progress. By understanding the strengths and limitations of breeding value estimates, breeders can make better-informed decisions that support productivity, animal health, adaptation, and long-term genetic improvement.
