Genetic gains

The expected response to selection or rate of genetic gain within one generation can be predicted by calculating the average BVs of the individuals‟ chosen as parents. However, breeders are often interested in predicting response over a longer time frame than one generation. The change in the genetic merit from one generation to the next is:

Response per generation = (standardized selection differential × accuracy of selection

× genetic standard deviation)

or Response per generation = ir_TIσ_T where, i= selection intensity,

rTI = accuracy of selection and

σT = genetic standard deviation.

In dairy cattle breeding, one sire can be used to inseminate more than 100,000 cows in a year, so sire selection typically has a greater impact on genetic gain, than female selection. However, it should be remembered that both sexes of parents contribute half their genes to the offspring.

Responses per year can be calculated by dividing response per generation, by generation interval. Thus,

ΔG= (response per generation)/(generation interval) Or, L σ r i ΔG TI T _

The young bulls and heifers are taken in the breeding herd on the basis of their parent‟s performance in order to be chosen as a potential parent for next generation. The decision as to which of these animals actually will pass their genes on is made on the basis of some measurement of their own genotype - by own performance in cows and by progeny tests in bulls. Genes are transmitted to the next generation in

four ways, which are: bulls to breed bulls, bulls to breed cows, cows to breed bulls and cows to breed cows.

By using the three determinants of superiority of selected animals, that is intensity of

selection (i), accuracy of selection (r2= R) and standard deviation of genetic values (σg) with four pathways or opportunities for selection results in Rendel and

Robertson (1950) the annual rate of genetic gain (ΔG) in dairy industry can be calculated as: CC CB BC BB g CC CC _ CB CB _ BC BC _ BB _ L L L L σ r i r i r i r i ΔG BB

Where, subscripts BB, BC, CB and CC refers to the bulls to breed bulls, bulls to breed cows, cows to breed bulls and cows to breed cows pathways respectively; the term in the denominator (eg LBB) refer to the lengths of

generation interval; i denotes to the intensity of selection and r is the measures of the accuracy of selection in respective pathways (Note r2= R where R is the reliability of an estimated breeding value or breeding worth) and σg is the genetic standard deviation of the selection objectives.

Several authors (e.g. Smith, 1962 and Powell, 1977) have been used different method for the estimation of genetic gain for the economically important traits of dairy cattle. Smith (1962) estimated the genetic gains based on the regression of daughter performance on time. Burnside and Legates (1967) developed a method to adjust first-lactation records of first-born fuI1 sisters for favorable environmental effects that bias estimates of genetic gain from Smith (1962) equations. Schaeffer et al. (1975) and Powell et al. (1977) have been estimated genetic gain by regressions of sire‟s average genetic merit on time. However, in all methods the rate of genetic gain could be improved by increase the accuracy and/or by reproductive developments that allow more offspring per sire (eg AI), more offspring per dam (eg MOET), or a reduction of the average age of the parents.

The factors that affect the rate of genetic gain:

1. Intensity of selection - determined by the proportion of available animals selected as replacements. This depends upon the number selected, also availability.

2. a factor derived from regression of true on estimated genetic merit. This factor usually includes heritability, but may be more complicated than shown the simplest case of direct selection.

3. The genetic standard deviation - a measure of the extent of genetic differences amongst animals in the population. This term really measures the amount of raw material currently available in the population. This factor is the most difficult to manipulate, other than by increasing genetic variation from the immigration of new genes.

4. The generation interval - a measure of how quickly progeny will be allowed to replace their parents.

It is tempting to use the above list to come up with a set of rules for increasing the rate of genetic gain. For example, response will be increased by:

a) increasing selection intensity. This is achieved by reducing the proportion selected, through making more animals available for selection, or through using fewer parents (especially males).

b) reducing the generation interval. This turns over the generations faster and can be achieved by using animals as parents at an earlier age, and by culling parents before becoming too old.

c) Increasing reliability of estimated genetic merit. This can be achieved by collecting more phenotypic information on an animal or its relatives, for example by progeny testing.

However,

These factors (a), (b) and (c) interact, the resulting in a net effect of changes when interactions are involved. For example:

Culling at a younger age will reduce generation interval. However, it will also increase the number of replacements required to maintain population size. The need for more replacements will increase the proportion selected and reduce the intensity of selection.

Using animals initially at a younger age, e.g. yearling bulls and heifers.This will reduce generation interval, but may also decrease the reliability of selection. Selection on fleece traits usually requires waiting until after hogget shearing, beyond the time at which mating has normally occurred.

Progeny testing animals to increase reliability of ranking will usually increase the generation interval.

Rendel and Robertson (1950) showed that the contribution of the four paths of genetic progress to total genetic progress were:

) I I I (I ΔDD) SD DS SS ( ΔG DD SD DS SS

Where, SS = Sires of sires DS = Dams of sires SD = Sires of dams and DD = Dams of dams

Sires of sires are used by AI organisations to produce young sires for sampling. The SS contribute most to genetic progress, as they are few and highly selected. In addition to intensity of selection being high for SS, the accuracy of selection is also high because all SS are progeny tested and rTI

ranges from 0.7 to 1.0.

Dams of sires are highly selected from out of the 2% of all cows in the population. Accuracy of selection is less than the SS path, ranging from 0.5 to 0.65.

Sires of dams are already chosen but farmers have limited option to select the sires to breed their cows. Intensity of selection is high but less than for SS.

The rate of genetic gain is dependent on the number of young bull tested with the cow population. For example, Robertson and Rendel (1950) showed maximum rate of improvement of 1.69% per year compared with 1% per year in a closed herd without progeny testing when 40 bulls mated with 1200 cows. Optimum number of progeny per tested bull maxmises the genetic gain per generation (Oliveir and Lôbo, 1995), they showed greater genetic gain can be achieved when 13 of total 550 available males are selected to be progeny tested with about 38 progenies per young sire.

Dams of dams are chosen by dairy farmers to leave female offspring in the population. The DD path contributes the least to genetic progress because the need of replacement females, results in a low selection pressure.

The actual progress is less than half the theoretical progress in dairy cows for milk production (Everett, 1983; Everett et al., 1976, Hintz et al., 1978 and Robertson and Rendel, 1950). The annual genetic progress in the registered Holstein female population was less than 1% of the mean (Lee et al., 1985). Maximum genetic gain was estimated at least 2% per year for artificial insemination populations of at least 10,000 cows (Specht and McGilliard, 1960). The theoretical limits are compromised in every path by decreases in the accuracy and intensity of selection. Accuracy is decreased by preferential treatment, and the use of non-AI sires. Intensity of selection is reduced by emphasis on secondary traits. Van Vleck (1977) reviewed potential causes for differences between theoretical and actual progress. However, gains in selection experiments exceeding theoretical expectations have other causes. A consensus from

several reports (Lofgren et al., 1985; Meland et al., 1982; Pearson et al., 1981; Powell et al., 1980 and 1983) indicates interaction of response to sire selection with herd means and variances. Also heritabilities increased with an average yield of herd (Pearson et al., 1981 and Powell and Norman, 1984). Interactions of genotype by environment are emerging as important for planning breeding strategies in developing countries, especially tropical areas (Abubakar et al., 1984, McDowell, 1983 and 1985) and in poor environments in Asia and Africa, the ¾ Holstein crossbreds were equal or exceeded slightly F1 crosses in milk yield. However, they

had a higher mortality rates, lower reproductive performance and shorter herdlife (Katapatal, 1979 and Kiwuwa et al., 1983, McDowell, 1983 and 1985 and Trail and Gregory, 1981).

In practice, every situation needs to be addressed individually using appropriate equations to predict the rate of genetic gain. Factors such as reproduction (age at puberty, reproductive capability) and measurements (time of recording relative to mating, sex limitating traits, eg milk yields) would impact the effectiveness of alternative strategies for a selection programme.

2.4.10 Summary

genetic gains provide knowledge that show the selection programme as effective.

genetic gain is optimised by balancing increasing selection intensity, reliability and generation interval.

actual progress is less than half the theoretical progress of dairy cows for milk production.