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Hello, and welcome to another video from the Animal Breeding module,

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 where we will talk about the Selection Effect, or the Genetic Gain.

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The selection effect, as shown in this slide,

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is a fundamental concept for the selection of both livestock and domestic animals,

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it is the so-called genetic gain (ΔG) and is based on the additive effect of genes.

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The first to study the selection effect was the English philosopher,

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doctor and mathematician Francis Galton. He studied height in families,

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comparing the average height of parents with the average height of their offspring.

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And he found that variation in individual traits is never transmitted to offspring in whole, but only in part.

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That is, there is a tendency in the offspring to return to the population average.

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He termed this effort as genetic homeostasis, or the effort to maintain an equilibrium state in the population.

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Francis Galton found that for body height, on average 2/3 of the variance is inherited,

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we know that this is the number of heritability, or the number of coefficients of heritability.

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And the return to the population mean is 1/3 (called the regression number).

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These values are different for each trait and each population, according to F. Galton.

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The selection effect can be expressed in two ways.

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The first approach is shown in the left part of the slide.

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It is an expression using a correlation field, or observation field,

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where the x-axis shows the individual values of one generation, here the generation of the parents,

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and the y-axis shows the values of their offspring.

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If we run a regression line through that field and plot the average values of the population of parents

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and offspring on that line, as shown in the figure,

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we get the difference in the trait between the two generations, that is, the selection effect.

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The second approach is expressed on the right part of the slide,

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and we will discuss this approach in more detail in the next slides.

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The selection effect is best explained from the following figure.

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The figure shows a normal distribution curve that represents the base population

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from which parents are selected for further reproduction, here shown by the hatched area.

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The mean of the base population is denoted as X with a bar

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and the mean of the selected individuals is denoted as X1 with a bar.

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The difference between these two means (X1-X) is called the selection difference

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and is usually denoted as the difference between the mean of the selected parents

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and the mean of the whole base population.

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The second curve of the normal distribution represents the population

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of offspring of the selected parents with the mean y with a bar.

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The selection effect is therefore the difference between the average performance

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of the offspring of the selected parents

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and the average performance of the base population from which the parents were selected.

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In other words, how much better the next generation of offspring is than the base population.

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Up to this point, we have been talking about the selection effect realized

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because we already have the subsequent generation of offspring and their performance

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and can calculate the size of the selection effect accurately.

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In animal breeding, however, we are more interested in predicting the future performance

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of offspring so that we can plan and select parent pairs correctly.

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The expected selection effect is used for this reason,

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and it is the expected selection effect that allows us to predict the performance of the subsequent generation.

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The slide here shows all the most basic relationships for calculating the expected selection effect.

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As it was clear from the above mentioned graph the simplest estimation is based on the relationship

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between the selection difference and the heritability coefficient.

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It should be remembered that selection is in most cases based on phenotypic values,

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and because genetic gain or selection effect is a genetic parameter of the population.

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The only possible convert between phenotypic values and the genetic basis of the population

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is the heritability coefficient.

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However, it is not always possible to work directly with phenotypic variation of selected individuals,

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or so-called selection difference. One example is milk yield and the selection of bulls that do not show 

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the performance. Nevertheless, it is necessary to determine the expected selection effect.

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For this purpose, another relationship is used, which again includes the heritability coefficient,

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plus the phenotypic standard deviation and the selection intensity,

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which is the standardised selection difference,

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i.e. the ratio of the selection difference to the phenotypic standard deviation.

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As this is the standardised selection difference, this value can be obtained from the relevant tables.

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Again, phenotypic variability expressed in terms of phenotypic standard deviation is not always available.

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Another possibility is to estimate the realized selection effect using additive genetic variability. 

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However, because genetic variability cannot be obtained directly,

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as with phenotypic variability expressed in terms of phenotypic standard deviation,

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the accuracy of its estimation must be taken into account,

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so the accuracy of the genetic merit of the individual, or breeding value,

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is also included in the calculation.

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The slide here shows a selection from a large table of standardized selection differences.

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It is clear from the table that the smaller the proportion of individuals

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we select the higher the value of selection intensity we get,

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and because there is a direct proportionality between selection intensity

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and selection effect we also get a higher selection effect.

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The table also shows that the size of the population we are selecting from also matters.

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If the size of the population is 10 or 50 individuals and we select in both cases,

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for example, 50% of the population, we will get different values of the selection intensity

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and therefore different values of the selection effect.

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Note also that the values of the selection intensity between populations with 50 individuals or very large populations,

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here represented by the infinity symbol, are already negligible.

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This means that further increases in populations do not significantly increase the value of the selection intensity.

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It should also be noted that in some cases it is necessary to compare

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the level of selection effect between two species of animals with different performance,

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for example milk yield (in kg) and fertility (in the number of offsprings).

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For this purpose, the so-called relative selection effect,

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which is expressed in units of phenotypic standard deviation, is used.

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The selection effect determined based on the formulas presented so far

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corresponds to the effect achieved in one generation.

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Due to the overlapping of generations, it is therefore more convenient

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for practical purposes to estimate the selection effect in one year.

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In this case, the value of the selection effect according to the above relationship is divided by the generation interval.

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And will therefore correspond to the relationship shown in this slide.

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Where the generation interval represents the average age of the parents

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at birth of their offspring that in their turn will produce the next generation of breeding animals

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 From this definition, it is clear that the generation interval will take on different values for different species,

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and even for different breeds of the same species.

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Another important part of the selection effect is the possibilities of increasing this selection effect.

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The first factor is the proportion of the population that should be selected from base population,

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the second factor is the magnitude of the phenotypic standard deviation of the trait that is being improved.

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Last but not least is the coefficient of heritability.

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The relationship between the variability and the magnitude of the selection differential

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and consequently the selection effect is shown in this figure. In this figure,

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three populations are shown by normal distribution curves of phenotypic values.

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The proportion of individuals selected for further breeding or selection is shown in shaded lines.

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Given the same population distribution, the selection differences matters.

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The fewer or higher the selection intensity only affected magnitude of selection effect.

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However, the intensity of selection cannot be increased indefinitely

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and the intensity of selection is influenced by the needs to maintain herd rotation.

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Another possible approach to estimate the selection effect is to compare

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the selected population with a control,or unselected, population. As shown in the slide here.

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By dividing the number of generations of selection, when we compare the selected population

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and the control unselected population, we are able to get a value for the response to selection,

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or the selection effect. It should also be remembered that even the unselected population

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is subject to natural selection and its performance may change over the generations.

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Thank you for your attention and I look forward to seeing you again for more videos.