Genetic Change

Paul L. Charteris
Institute of Veterinary, Animal and Biomedical Sciences,
Massey University, Palmerston North, New Zealand

Improvement in farm profitability through genetic change of livestock relies on the ability to identify animals that are better for certain traits followed by selection pressure to improve these traits.

Individual breeders undertake the first part of the challenge - namely identifying which traits influence farm profit, whilst animal breeding technologies help to quantify the genetic merit of animals, i.e. calculate EBVs. Breeders in-turn use these EBVs as a selection tool to achieve genetic change. There is little benefit in achieving rapid genetic advance if this change is not directed at traits of economic importance.

This issue of “Breeding Matters Beef Supplement” explores Genetic Evaluation - the technology used to quantify the genetic merit of animals and rank them. An overview is provided of factors affecting genetic change. Some features of genetic prediction, together with the place of genetic evaluation within New Zealand’s cattle industries are outlined. Finally, the use of Internet technologies that relate bull-buying decisions to farm profit are explored.

Genetic evaluation is only one stage of a breeding programme - it fits in the picture after you have established your breeding objective (deciding which traits are important and the relative selection importance of each trait) but before selection or culling decisions are made.

Most modern genetic evaluations such as Group Breedplan show the direction and rate of genetic trend for a breed denoted by the change in Estimated Breeding Values (EBVs) over time. The fact that EBVs do change over time indicates these characters have been selected for - at least by the herds participating in the analysis. What influences the direction and rate of genetic change ?

First, the direction of genetic change is established by the breeding objective of the breeder that is they decide which traits are economically important and how much selection emphasis is apportioned to each trait in the objective. Second, the rate of genetic change is determined by four factors in what is often referred to as the key equation:

Genetic advance per year =

Selection intensity measures how choosy you are in selecting your replacement dams and sires. Your choosiness is reflected in the difference in merit (breeding value) between the animals chosen to be parents and all available parents.

Accuracy of Selection is the strength of the relationship between the true breeding value of an animal (true BV) and our prediction of it (the EBV) is usually measured as the correlation between the two values. In genetic evaluations, this correlation (or a derivative there of) is termed EBV accuracy1. The heritability of a the trait and the amount of measured information on the animal and its relatives all determine the accuracy. The rate of genetic advance will be improved when accurate EBVs are available.

Genetic variation is represented as the standard deviation of BVs. An increase in standard deviation of BVs suggests there are more individuals which would be classified as outliers (either very high or poor merit) providing greater opportunity to select or cull animals at the extremes. Genetic variation is not easy to influence in a population (it is difficult to increase the variability of marbling in the national beef cattle herd). Some practices such as outbreeding, linebreeding or consistent selection for one trait over many generations can theoretically change the level of genetic variation. In practice, the decrease in genetic
variation due to selection or linebreeding is expected to be minimal.

Generation interval is the average age of parents when their progeny are born. For beef cattle, generation interval is typically around 5 years. Generation interval can be thought of as how quickly one generation replaces the previous one. A decrease in generation interval (i.e. using younger parents) will increase the rate of genetic advance per annum provided superior merit animals are consistently being selected for.

Within any herd with good reproductive performance there is limited opportunity to markedly affect the intensity of selection, level of genetic variation or generation interval. Thus, three of the four factors influencing genetic advance are largely invariant due to management constraints. The remaining factor, accuracy of selection can be markedly affected by genetic prediction. This topic is covered in the next section.

Genetic Prediction - BLUP

Accuracy of selection really means how well we know the true Breeding Value (true BV) of an animal. We never know the true BV exactly but we can estimate this value from the data we have available. These estimates are called Estimated Breeding Values (EBVs). The process of deriving these EBVs is termed genetic prediction. Using EBVs when choosing breeding animals helps avoid selecting animals which are superior due to superior management and similarly helps avoid culling animals that look bad due to environmental effects.

From the viewpoint of bull-breeders and bull-buyers, EBVs enable the highest merit animals to be identified and selected for, thereby annually improving the average merit (and hopefully profitability) of the herd. Potential beef industry benefit from genetic improvement is dependant on the accuracy with which superior animals are identified and selected for, and the culling of inferior animals. A nil accuracy of selection (no EBVs for traits of economic importance) would likely result in haphazard industry improvement for these traits whilst high accuracy EBVs would likely provide considerable scope for improvement.

Genetic prediction a little is like owning a car. You do not need to know everything that makes the car run but you do need a few key indicators such as how much fuel is left in the tank. Similarly, with genetic evaluation, it is not necessary to know how EBVs are derived (the process gets very mathematical) but you do need to know how these values can be used to make (profitable) selection decisions. However, when we look under the bonnet at how these EBVs are calculated we find the genetic engine has the following features.

The method of choice for predicting breeding values of animals across herds is known as Best Linear Unbiased Prediction (BLUP). BLUP is a class of statistical tools that have some desirable properties for predicting things like breeding values. Indeed, the “Best” term in the acronym BLUP is used to describe the property that from the available data on an animal, its EBV will be as error-free as possible. The “Linear” term simply means the data has not been adjusted to some other scale such as being squared.

“Unbiasedness” means that on average, the EBV calculated will be the same as the animals true BV.
“ Prediction” refers to the task at hand, that is we are trying to predict true BVs.

BLUP methodologies are able to account for genetic differences between groups of animals. This is achieved by comparing the performance of relatives in different groups. In addition all possible data sources are used to calculate EBVs, these sources include information from all relatives and records from other correlated traits to predict the trait of interest. Non-random mating (such as mating your best bull to only the top cows) can be accounted for by adjusting the animals EBV for the EBV of its mates. Most modern evaluations allow comparison of genetic merit (as EBVs) across herds and years. If suitable
genetic links (bulls with progeny in different herds or countries) exist between groups of animals, EBVs can be compared across countries and breeds.

Industry Wide Evaluation

Genetic prediction technology becomes most useful to a livestock industry when it is applied in the form of an industry-wide genetic evaluation allowing comparison of the genetic merit of animals across flocks or herds and between countries and breeds. By necessity, such an evaluation demands the establishment of a computer database containing performance and pedigree information. Such a database can be applied to tracing individual animal health status and production records as well as genetic evaluation.

Figure One: Beef cattle breeding industry structure


The beef and dairy cattle performance recording and genetic evaluation structures in New Zealand are shown in Figures One and Two. Within the beef cattle industry approximately 4% of cattle are performance recorded, these exist almost exclusively within the bull-breeding sector. Performance and pedigree records from these cattle are transferred to separate databases maintained by individual Breed Societies. Data from Breed Societies are transferred to a Genetic Evaluation Service. In New Zealand beef cattle Breed Societies have contracted either Breedplan or Colorado State University to provide this service. The genetic evaluation service in-turn delivers a set of EBVs to their customers - those Breed Societies it is directly contacted to. Genetic evaluation reports are delivered to participating herds and sire summaries distributed to all potential bull-buyers.

Figure Two: Dairy cattle industry breeding structure


The dairy industry comprises 2.9 million dairy cows, virtually all of which are voluntarily pedigree and performance recorded. These records are maintained on a single national database maintained on behalf of the industry by Livestock Improvement Corporation (LIC). The same organisation delivers the results of genetic evaluation in the form of Breeding Worths (BWs) to all participating dairy producers. In addition, LIC provides an advisory service supporting record keeping, mating and management decisions.

A large number of recorded crossbred cows (19% of national herd) permits comparison of BWs across breeds.

The major differences between the dairy and beef recording industries are that only a small subset in the beef cattle producers are actively performance recording. In addition, the dairy model groups the national database together with genetic evaluation, research and advisory services co-ordinated by one organisation, namely LIC. The major benefits of the dairy model is that the genetic evaluation service receives quality inputs of data through herd testing of virtually all herds, has co-ordinated research support to drive innovation and also advisory support to interpret BWs on behalf of end-users comprising commercial producers.

Figure Three: Alternative beef cattle breeding industry structure


A possible alternative beef recording and evaluation system is shown in Figure Three. In this model, data from industry comprising; commercial producers, beef processors and bull-breeders are transferred to a national beef cattle database. A genetic evaluation service could provide EBVs to bull-breeders and Breed Societies in addition to performance and management reports to individual herds and to processors. Such a structure would enable permit more accurate EBVs on registered cattle and a greater variety of EBVs by using beef processor data. Other inputs and users of a national database include trade and legislative bodies, animal health authorities and research providers.

Charolais bulls captured

New Zealand Charolais bulls have been captured and penned on the world wide web. A website enables bull-buyers to select bulls which will increase profit for their own farming conditions.

Previously, bulls have had EBVs for some criteria of economic importance but it has been difficult to assess the relative importance of these EBVs. For example a farmer may wish to know the relative importance of milking and mothering ability of a breeding cow compared with growth rate of her progeny through to harvest age.

In a world first for beef cattle sire selection, prospective bull buyers can use this website to specify their own farming conditions and then select bulls which will improve profit for the these conditions. This is achieved by defining breeding objectives for a range of beef cattle farming and marketing conditions.
Second, based on the above objectives, selection indices were derived comprising a list of selection criteria (EBVs) and weighting these so that they best predict each breeding objective.

Essentially, these Charolais bulls are ranked on an index value (the product of weighting factors and the bulls own EBVs) which collates all their existing EBVs into a single dollar value. Bulls with the highest index values would be the most profitable for a particular farming environment.

Prospective bull-buyers are offered additional choices of intended bull role (general purpose or terminal sire) and age at which they intend to sell progeny. Options exist for placing upper and lower limits on EBVs and selecting only bulls of a particular age group. This web-based sire selector takes a lot of guesswork out of bull selection. It weights each of the bulls EBVs according to its contribution to farm profit.

The advantages of using web-based technologies are that users can access this site from home and select bulls prior to inspecting them in the paddock. They (the bull-buyers) will already have an indication of a range of bulls which are likely to improve farm profit prior to making their final selection decisions.

Charolais Sire Selector: http://www.charolais.org.nz/sireselector

Funding for Breedplan Research and Extension Support is provided by the Meat Research and Development Council (MRDC)