GENETIC IMPROVEMENT

This article was originally printed in the June / July 2001 issue of Tracking The Industry. . .
This article is copyrighted and may not be reproduced in any form without permission.

Introduction

Genetic Improvement is a process with a number of essential components, which should be carried out or developed in a particular order. These components, which will each be discussed in detail, include a breeding objective, genetic parameters, trait measurement, genetic links between herds, pedigree recording, EPD calculations, and selection and dissemination of superior genetics.

Breeding Objective

The first step is the development of a breeding objective, which is a statement of what is important, i.e. what traits need to be improved. In some cases, the breeding objective may consist of a single trait, but it is usually comprised of a number of traits of importance, e.g. antler size and shape traits might be combined with growth, carcass, reproduction or longevity.

When the breeding objective consists of more than one trait, the relative importance of those traits must be determined, i.e. we have to know not only which trait is the most important but how much more important than the next one it is. There are a number of ways to perform these calculations, but one relatively easy method is to develop a spreadsheet model of the costs and returns of deer farming. The model is then set to determine the impact of changes in each trait (e.g. a 10% increase in fawning rate) on the profit earned by the operation. The development of this model would require some consensus among breeders on the parameters used in the model (e.g. feed costs, feed consumption, velvet weights, sale prices, etc.).

Genetic Parameters

Another essential component of a Genetic Improvement program is a set of genetic parameters. Most livestock producers are familiar with the concept of the heritability of a trait, which is a measure of the relative importance of genetics versus the environment. Traits with low heritabilities (e.g. reproductive traits) are heavily influenced by the environment, while traits with medium (e.g. growth traits) or high (e.g. conformation traits) are determined to a higher degree by the genes that an animal has, and are therefore passed more predictably from one generation to the next. The higher the heritability of a trait, the faster will be the rate of genetic improvement when the trait is selected for.

The heritabilities in the following table were taken from papers in the scientific literature and were estimated from a number of deer species, including white-tails.

Genetic correlations are another type of genetic parameter. A genetic correlation is an indication of how selection for one trait affects other traits. Genetic correlations can be either positive or negative. For example, if selection for large antlers leads to increased body weight, then they are positively correlated. In many species, selection for growth has an adverse effect on reproduction, which means they are negatively correlated.

Genetic correlations reported in the scientific literature appear to be high and positive for the following combinations of traits:
- birth and weaning weight
- a variety of postweaning weights
- velvet antler weights in successive years
- body weight in males and velvet antler weight in males
- body weight in females and velvet antler weight in males
This is an extremely favourable set of circumstances, as selection for body weight in either males or females should lead to increased antler weights in males, and selection for body weight or antler weight at young ages should produce animals that are above average at later ages as well.

Trait Measurement

Objective measurements using a device such as a scale, a yardstick or a measuring tape are preferred over subjective or 'eyeball' assessment of an animal's performance. However, it is possible to select animals for traits where a subjective assessment is converted to a numerical score, e.g. body condition scores where an animal's condition is rated on a scale of one to five.

It is desirable to measure as many of the traits in the breeding objective as possible, but where measurement of a trait is impossible, that trait can still be improved by measuring other traits that are positively correlated. For example, breeders of beef cattle, sheep and swine use ultrasound to measure fat depth and muscle depth to evaluate animals for the amount of meat and fat in their carcasses, without having to kill the animals.

Genetic Links Between Herds

It is relatively easy for breeders to determine which of their own animals are the best for a given trait, but problems arise when it comes to purchasing outside genetics, whether live animals or semen. Physical differences between the best buck in one herd and the best buck in another herd may be due to superior genetics - or may simply be the result of a better feeding program! For breeders to feel confident that they are taking a step forward and not backward, it is necessary to have some method of directly comparing animals in different herds.

This is really the whole point of a genetic improvement program and the crucial component that allows this direct comparison is the provision of genetic links between the herds, i.e. there must be some animals in each herd that are related to animals in other herds. These animals act as genetic benchmarks, and allow the direct comparison of each of the animals in all of the herds on the program. This will be further illustrated with an example.

If Buck A in Herd A sires fawns that weigh 40 lb. at weaning, and Buck B in Herd B sires fawns that weigh 35 lb. at weaning, it is tempting to assume that Buck A is genetically superior to Buck B.

However, if the feeding program in Herd A is vastly superior to that in Herd B, then the difference between the offspring of the two bucks could be due to the environment (feeding), not to genetics. To separate the two, it is necessary to have some animals in Herd A that are related to some animals in Herd B.

If it were possible to breed some of the does in each herd to a third buck, Buck C, then the genetic links created by Buck C would allow us to separate the effects of genetics from the effects of the environment. The table below shows one possible outcome from such a scenario:

Consider the results in Herd A (middle column). Buck C's offspring are heavier than those of Buck A, and all the fawns in this column of the table were raised in the same environment (Herd A), so we conclude that Buck C is genetically superior to Buck A.

Now look at the results in Herd B. Buck B's fawns are heavier than the offspring of Buck C, and all those fawns were raised in the same environment (Herd B), so we conclude that Buck B is genetically superior to Buck C.

Now, if Buck B is better than Buck C, and Buck C is better than Buck A, we can also conclude that Buck B is better than Buck A, and that the higher numbers in the Herd A column are the result of better feeding, not better genetics.

This is an extremely oversimplified example of how genetic relationships between herds allows us to compare the genetic merit of animals in different herds; in practice the calculations required to sort out the genetic and environmental factors that affect the performance of animals in a dataset, and then assign estimates of genetic merit to these animals are very complex.

Pedigree Recording

Of course, in order to make use of the genetic links between herds, pedigrees have to be recorded. The ideal situation occurs when sires and dams of all animals are known, but a less complete genetic evaluation could also be done if only the sires were known. It is also important to be able to pinpoint the date of birth to within a few days, as most traits are adjusted for the actual age of an animal when the trait is measured. In other words, it wouldn't be fair to weigh two animals on the same day and compare the weights if one is several weeks or months older than the other, without making some adjustment for their actual age.

EPD Calculations

Once the pedigree and performance data has been collected, it is combined with the genetic parameters to calculate Expected Progeny Differences (EPDs). An EPD is an estimate of the superiority of an animal's offspring, relative to the offspring of an average animal in that population. For example, if a buck has an EPD of 1.0 kg for weaning weight, it is expected to have offspring that weigh 1.0 kg more than the offspring of an average buck (one with an EPD of 0) and 2.0 kg more than the offspring of an animal with an EPD of -1.0 kg.

EPDs can be used to compare animals in different herds, but also animals of different sexes and ages, e.g. EPDs can be used to compare the genetic merit of a five-year-old buck in one herd with a nine-year-old buck in another herd.

While producers rarely make choices between a male and a female animal, it is important to remember that does carry just as many genes for antler size and shape as bucks do, even though they do not produce antlers themselves. Animals inherit half of their genes for each trait from their sire and half from their dam, whether the parents express the trait themselves or not. Dairy farmers routinely purchase semen from bulls that are expected to pass on superior genes for milk production to their daughters, even though the bulls have never produced a drop of milk themselves!

Selection and Dissemination of Superior Genetics Once the EPDs have been calculated, they can be used to rank available animals from the best to the worst, and the best animals can be selected as parents. If the breeding objective includes more than one trait, the EPDs for each can be combined into some sort of index that weights each EPD by its relative importance, and that index can be used to rank the animals, rather than any single EPD.

Identification of the best bucks would probably be followed by having semen collected on those bucks and made available for purchase by other breeders. In this way the superior genetics are disseminated to the rest of the industry, and the industry as a whole makes genetic improvement for the traits in the breeding objective. The EPDs also contribute to genetic improvement by giving breeders a more accurate way to choose the best bucks and does to be kept as replacements within their own herds.

A Model of a Successful Genetic Improvement Program, The Western Suffolk Sire Reference Program, is a genetic improvement program that has been in operation in Alberta since 1995, consisting of 11 Suffolk (sheep) breeders from Lethbridge to Flatbush. Each spring the members of the program chooses two reference sires, rams that will be used in each flock by AI to create the genetic links necessary for EPD calculations.

In the fall, each flock AIs 15 ewes to those rams. The ewes lamb in the winter and in the following spring all of the lambs in the flock (not just the AI lambs) are weighed and ultrasounded for fat and muscle depth. EPDs are calculated for weight, muscle depth and fat depth, and these EPDs are combined into a Lean Growth Index, designed to obtain maximum genetic improvement for weight and muscle, without increasing fat. This index is used to rank all of the rams in all eleven flocks, and two more reference sires are chosen for the next year from the rams at the very top of the group.

In the six years this program has operated, it has achieved a genetic improvement of 23% for Lean Growth. This has resulted from a) the widespread use of the reference sires (superior genes) in each of the 11 member flocks and b) the greater accuracy with which the breeders can now select ram and ewe replacements from their own flocks, or the flocks of another member, using the EPDs. This extraordinary rate of genetic improvement has been achieved in spite of a number of problems that the group has encountered in collection of semen from reference sires, and poor AI conception rates. It shows what can be done when sires are assessed for traits based on the performance of a large number of offspring, rather than just on their own performance for a trait.

Association Possibilities

If the membership of an association were interested in starting a program similar to that outlined above, it would be necessary to look at each aspect of a genetic improvement program, and determine what components were already in place and which ones needed to be worked on.

A breeding objective could be arrived at fairly readily with a computer spreadsheet model, providing breeders could arrive at some consensus on costs and returns of production and other model parameters (feed consumption, reproduction rates, etc.)

There are enough genetic parameters in the scientific literature to start a program. After a few years of data collection, an association's own data could be used to estimate parameters specific to the provincial population.

The genetic parameter estimates reviewed above are very favourable, with most of the traits of economic importance having very high heritabilities. The high and positive genetic correlations indicate that selection for any one of the desired traits would result in genetic improvement in other traits as well.

Measurement of some traits (e.g. body weights) may be problematic, but antler traits should be relatively easy to record. Measurement for additional traits will increase the accuracy of selection for all traits, and should be pursued where possible.

If exact pedigrees are not possible, then sires must be known. Without parentage information, genetic evaluation is reduced to just measuring individuals and choosing the ones with the highest numbers.

EPD calculations are available on a fee-for-service basis here in Alberta. If an association were to collect, organize and edit the data from all of the participating herds into a single computer file, then it could be analyzed once per year at a relatively low cost.

The use of AI is already quite common in Canada. This is a big advantage for your industry, as the use of AI to create genetic links between herds, and to disseminate genetic improvement, is essential to the success of such a program.

There do not appear to be any factors that would make it impossible for an association to pursue an organized genetic evaluation program. The most important component of such a program, however, is the commitment of the breeders who participate in it. Programs such as this do run into obstacles in their development, and these problems can be dealt with, but only if the participants are completely committed to the success of the program and are in it for the long term.

Cathy Gallivan, PhD

Box 4, Site 8, RR#1
Olds, Alberta
T4H 1P2
403-224-3962
gallivan@sheepcanada.com

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