Introduction

Definitions and concepts

Definitions of terms and concepts
The science of genetics consists of three broad areas:

Classical Genetics (based on Mendel's 1st and 2nd laws)

Classical genetics mainly concentrate on:

1. Mendel's first and second laws of inheritance

 Mendel’s law of segregation states that the two alleles for a heritable
character separate and segregate during gamete production and end
up in different gametes
 Mendel’s second law states that when two or more characteristics are
inherited, individual hereditary factors assort independently during
gamete production, giving different traits an equal opportunity of
occurring together.
2. Meiosis and mitosis
 Meiosis is a process where a single cell divides twice to produce four
cells containing half the original amount of genetic information while
mitosis Mitosis is a process where a single cell divides into two
identical daughter cells (cell division). During mitosis one cell divides
once to form two identical cells. The major purpose of mitosis is for
growth and to replace worn out cells
3. Somatic and sex chromosome mapping
 Chromosome mapping is the assignment of genes to specific locations
on a chromosome. A gene map serves many important functions and
is much like understanding the basic human anatomy to allow
doctors to diagnose patients with disease. A doctor requires
knowledge of where each organ is located as well as the function of
this organ to understand disease. A map of the human genome will
allow scientist to understand where genes are located so that its
function within the human genome can be elucidated. Gene mapping
can provide clinicians with useful information regarding genes that
are linked, or segregate closely together.

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 2. Extrachromosomal inheritance

 This is inheritance of traits through DNA that is not connected with

the chromosomes but rather to DNA from organelles in the cell. Also
called cytoplasmic inheritance. The control of phenotype of the
progeny by the non-nucleus component present in the cytoplasm is
called extrachromosomal inheritance

3. Cytogenetics

 This is a field of study which analyses the number and structure of

human and animal chromosomes. Changes that affect the number
and/or structure of the chromosomes can cause problems with
growth, development, and how the body functions.

4. Quantitative genetics

 This is the study of the inheritance of traits that show a continuous

distribution of phenotypes in a segregating population. A quantitative
trait is a measurable phenotype that depends on the cumulative
actions of many genes and the environment. These traits can vary
among individuals, over a range, to produce a continuous distribution
of phenotypes. Examples include height, weight and blood pressure. A
quantitative trait locus (QTL) is a section of DNA (the locus) that
correlates with variation in a phenotype (the quantitative trait). The
QTL is typically linked to, or contains, the genes that control that
phenotype.

Molecular Genetics (based on the Central Dogma of Molecular Genetics)

1. Structure of nuclear and organelle DNA

 The structure of a nucleus contains a nuclear membrane,

chromosomes, nucleolus and cytoplasm. The cell nucleus contains
much of the cell's genetic material in form of DNA molecules
organized into structures called chromosomes.

2. Chemistry and replication of DNA

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  Each single nucleotide has a compound called phosphate, a sugar
(deoxyribose) and a base. The phosphate and sugars are the same in
all DNA nucleotides, but the bases are not. Thymine, adenine,
guanine, and cytosine are the four DNA bases.
 DNA replication is the process by which a double-stranded DNA
molecule is copied to produce two identical DNA molecules

3. Transcription and translation

 Transcription is the process of making an RNA copy of a gene

sequence. This copy, called a messenger RNA (mRNA) molecule, leaves
the cell nucleus and enters the cytoplasm, where it directs the
synthesis of the protein, which it encodes.
 Translation is the process of translating the sequence of a messenger
RNA (mRNA) molecule to a sequence of amino acids during protein
synthesis. The genetic code describes the relationship between the
sequence of base pairs in a gene and the corresponding amino acid
sequence that it encodes.

4. DNA cloning

 Multiplication of copies of a piece of DNA

5. Control of gene expression

 Regulation of gene expression includes a wide range of mechanisms

that are used by cells to increase or decrease the production of
specific gene products (protein or RNA)
 Genes encode proteins and proteins dictate cell function. Therefore,
the thousands of genes expressed in a particular cell determine what
that cell can do. Moreover, each step in the flow of information from
DNA to RNA to protein provides the cell with a potential control point
for self-regulating its functions by adjusting the amount and type of
proteins it manufactures.

6. Mutation and the repair of DNA

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  Mutation is the spontaneous and permanent alteration of the
nucleotide sequence of the genome of an organism
 DNA repair is a collection of processes by which a cell identifies and
corrects damage to the DNA molecules that encode its genome

Evolutionary Genetics (based on the theory of Natural Selection

proposed by Darwin)

Darwin’s theory states that all species of organisms arise and develop through the
natural selection of small, inherited variations that increase the individual's ability
to compete, survive, and reproduce. The theory emphasizes that only the organisms
best adapted to their environment tend to survive and transmit their genetic
characters in increasing numbers to succeeding generations while those less
adapted tend to be eliminated. Natural selection is the gradual process by which
heritable biological traits become either more or less common in a population as a
function of the effect of inherited traits on the differential reproductive success of
organisms interacting with their environment.

1. Hardy-Weinberg equilibrium

 In a large random-mating population with no selection, mutation or

migration, the gene frequencies and the genotype frequencies are
constant from generation to generation. A population with constant
gene and genotype frequencies is said to be in Hardy-Weinberg
equilibrium.

2. Evolution

 Evolution is change in the heritable characteristics of biological

populations over successive generations. Evolutionary processes give
rise to biodiversity at every level of biological organisation, including
the levels of species, individual organisms, and molecules

3. Speciation

 Speciation is the evolutionary process by which biological populations

evolve to become distinct species.

4. Population genetics

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Introduction to the study of population genetics

Population genetics

Population genetics is a field of biology that studies the genetic composition of

biological populations and the changes in genetic composition that result from the
operation of various factors. It can also be defined as the study of the distributions
and changes of allele frequency in a population, as the population is subject to the
four main evolutionary processes: natural selection, genetic drift, mutation and
gene flow.

Definitions and basic concepts

Coalescence is the point at which common ancestry for two alleles at a gene
occurs in the past.
Effective population size is an ideal population that incorporates such factors as
variation in the sex ratio of breeding individuals, the offspring number per
individual, and numbers of breeding individuals in different generations.
Gene flow is the movement between groups that results in genetic exchange.
Genetic bottleneck is a period during which only a few individuals survive and
become the only ancestors of the future generations of the population.
Genetic drift is the chance changes in allele frequencies that result from small
population size
Neutral theory is the genetic change is primarily the result of mutation and
genetic drift, and different molecular genotypes are neutral with respect to
each other.
Population is a group of interbreeding individuals that exist together in time and
space.
Hardy-Weinberg principle: After one generation of random mating, single-locus
genotype frequencies can be represented as a binomial function of the allele
frequencies.
A simply inherited trait is a trait that is affected by only a few genes e.g. coat
colour and presence of horn. Such traits have two common characteristics.
First phenotypes for these traits tend to be “either/or’ in nature
(categorical/qualitative). Second, the traits are affected very little by the
environment (a cow either has horns or is polled; a Labrador is black,
chocolate or yellow).
A Polygenic trait is a trait affected by many genes, no single gene having an
overriding influence. Phenotypes for polygenic traits are typically quantitative
in their expression (show continuous expression). Such traits are generally
numerically measured e.g. 250kg weaning weights, 7500kg lactation yield,
etc.

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Causes of genetic change in a population

1 Population size: The genes passed from one generation to the next are a
sample of the genes in the parent generation. The gene frequencies are
subject to “sampling variation” between successive generations. The smaller
the number of parents, the greater the sampling variation.
2 Differences in fertility and viability (SELECTION): Different genotypes
among parents may have different fertilities, hence will contribute unequally
to the gametes of which the next generation is formed. Different genotypes
among newly formed zygotes may have different survival rates; hence gene
frequencies in the new generation may change.
3 Mutation: Defined as a sudden heritable change in the genetic make-up of
an individual.
4 Migration: The movement of individuals between populations. Generally,
movement of large numbers of livestock between populations is restricted,
but through use of techniques e.g. Artificial insemination and embryo
transfer genetic material is moved among populations.
5 Mating systems: Unless mating in a population is random (panmixia) i.e.
each individual has an equal chance of mating with any other individual in
the population, gene and genotypic frequencies will change from one
generation to the next.

Hardy-Weinberg equilibrium
Hardy-Weinberg law
In a large random-mating population with no selection, mutation or migration, the
gene frequencies and the genotype frequencies are constant from generation to
generation. These properties are derived from a theorem, or principle known as the
Hardy-Weinberg law after Hardy and Weinberg, who independently demonstrated
them in 1908. A population with constant gene and genotype frequencies is said to
be in Hardy-Weinberg equilibrium. The relationship between gene frequency and
genotype frequencies is that if the gene frequencies of two alleles among the
parents are p and q, then the genotype frequencies among the progeny are p2, 2pq
and q2, thus:
Genes in parents Genotypes in progeny
A1 A2 A1A1 A1A2 A2A2
Frequencies p q p2 2pq q2

Allelic and Genotypic Frequencies and the Hardy-Weinberg Principle

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Most genetics research focuses on the structure of genes on chromosomes, the
function of genes, and the process of genetic transmission from parent to
offspring.
Population genetics instead focuses on the overall gene pool in a population of
interbreeding organisms - that is, the frequency of all alleles of all genes in the
population - and whether the gene pool may be changing across generations in a
population. Population genetics examines the relationship among genotype
frequencies in a population, allele frequencies in its gene pool, and factors that
can change these frequencies over time.
Genetic Equilibrium and the Hardy-Weinberg Principle
A population is in genetic equilibrium when allele frequencies in the gene pool
remain constant across generations. A gene pool will be in equilibrium under the
following conditions:
• the population is very large
• individuals in the population mate randomly
• there is no migration into or out of the population
• natural selection does not act on any specific genotypes
• males and females have the same allele frequencies [vs. individuals are diploid
and reproduce sexually]
• no mutations occur
In 1908 Godfrey Hardy and Wilhelm Weinberg, working independently, specified
the relationship between genotype frequencies and allele frequencies that must
occur in such an idealized population in equilibrium. This relationship, known
as the Hardy-Weinberg principle, is important because we can use it to
determine if a population is in equilibrium for a particular gene.
Population Genotypes and Alleles
The Hardy-Weinberg principle applies to individual genes with two alleles, a
dominant allele and a recessive allele. A population with such a gene can be
described in terms of its genotype numbers - the number of individuals with each
of the three resulting genotypes - or in terms of the three genotype frequencies.
The frequency of each genotype is the number of individuals in the population
with that genotype divided by the total number of individuals in the population.
See Table 1.
Table 1: Genotype numbers and genotype frequencies in a hypothetical
population.

Genotypes Number with these Genotypes Genotype Frequencies

AA 180 0.36
Aa 240 0.48
aa 80 0.16
Total 500 1.0

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Calculating Allele Frequencies
We can also describe a population, somewhat more abstractly, in terms of its
allele frequencies. The frequency of an allele is defined as the total number of
copies of that allele in the population divided by the total number of copies of all
alleles of the gene. We can calculate population allele frequencies from genotype
numbers.
The total number of dominant A allele in our population equals 600, which is the
sum of:
- the number of AA individuals’ x 2 (the number of A alleles per individual =
180 x 2 = 360
- the number of Aa individuals (x1, the number of A alleles per individual +240
600

The total number of all alleles of the gene equals 1000, which is 2 times
the number of individuals in the population (since the individuals are
diploid).

By definition, the frequency of the dominant A allele in our population equals

600/1000, or 0.60.

We can calculate the total number of a alleles in the population, and divide
by the total number of alleles: (2x80 + 240)/1000 = 400/1000 = 0.4

Or, since the total of all the allele frequencies sums to 1.0 and since there are
only two alleles, A and a, we can calculate the allele frequency by subtracting
the A allele frequency from 1.0:

1.0 – 0.60 = 0.40

SELECTION

Selection can be defined as the process that determines which individuals become
parents, how many offspring they may produce, and how long they remain in the
breeding population. There are basically two types of selection;
1) Natural selection – selection that occurs in nature independent of
deliberate human control.
2) Artificial selection – selection that is under human control.

Natural selection
The theory of natural selection is the centre piece of The Origin of Species and of
evolutionary theory. It is this theory that accounts for the adaptations of
organisms, those innumerable features that so wonderfully equip them for survival
and reproduction; it is this theory that accounts for the divergence of species from
common ancestors and thus for the endless diversity of life. Natural selection is a
simple concept, but it is perhaps the most important idea in biology. If individuals

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having certain genes are better able to produce mature offspring than those
without them, the frequency of those genes will increase. This is simply expressing
Darwin's natural selection in terms of alterations in the gene pool. (Darwin knew
nothing of genes.) Natural selection results from
 differential mortality and/or
 differential fecundity.

Mortality Selection
Certain genotypes are less successful than others in surviving through to the end of their
reproductive period.
The evolutionary impact of mortality selection can be felt anytime from the formation of a
new zygote to the end (if there is one) of the organism's period of fertility. Mortality selection
is simply another way of describing Darwin's criteria of fitness (any trait that promotes
survival — at least until one's reproductive years are over — increases fitness. Such traits are
adaptations).

Fecundity Selection
Certain phenotypes (thus genotypes) may make a disproportionate contribution to
the gene pool of the next generation by producing a disproportionate number of
young. Such fecundity selection is another way of describing another criterion of
fitness. In each of these examples of natural selection, certain phenotypes are
better able than others to contribute their genes to the next generation. Thus, by
Darwin's standards, they are more fit. The outcome is a gradual change in the gene
frequencies in that population
Calculating the Effect of Natural Selection on Gene Frequencies
The effect of natural selection on gene frequencies can be quantified. Let us assume
a population containing
 36% homozygous dominants (AA)
 48% heterozygotes (Aa) and
 16% homozygous recessives (aa)

The gene frequencies in this population are

p = 0.6 and q = 0.4

The heterozygotes are just as successful at reproducing themselves as the

homozygous dominants, but the homozygous recessives are only 80% as
successful. That is, for every 100 AA (or Aa) individuals that reproduce successfully
only 80 of the aa individuals succeed in doing so. The fitness (w) of the recessive
phenotype is thus 80% or 0.8.

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Their relative disadvantage can also be expressed as a selection coefficient, s,
where

s=1−w
In this case, s = 1 − 0.8 = 0.2.

The change in frequency of the dominant allele (Δp) after one generation is
expressed by the equation

sp0q 0 2
p 
1  sq 0 2

where p0 and q0 are the initial frequencies of the dominant and recessive alleles
respectively. Substituting, we get

(0.2)(0.6)(0.4)2 0.019
p    0.02
1  (0.2)(0.4)2 0.968

So, in one generation, the frequency of allele A rises from its initial value of 0.6 to
0.62 and that of allele a declines from 0.4 to 0.38 (q = 1 − p).
The new equilibrium produces a population of

 38.4% homozygous dominants (an increase of 2.4%) (p2 = 0.384)

 47.1% heterozygotes (a decline of 0.9%) (2pq = 0.471) and
 14.4% homozygous recessives (a decline of 1.6%) (q2 = 0.144)

If the fitness of the homozygous recessives continues unchanged, the calculations

can be reiterated for any number of generations. If you do so, you will find that
although the frequency of the recessive genotype declines, the rate at which a is
removed from the gene pool declines; that is, the process becomes less efficient at
purging allele a. This is because when present in the heterozygote, a is protected
from the effects of selection.
General selection model
The General Selection Model (GSM) is a model of population genetics that
describes how a population's allele frequencies will change when acted upon by
natural selection.

where:
p is the frequency of allele A1
q is the frequency of allele A2

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Δq is the rate of evolutionary change of the frequency of allele A2
W0, W1 and W2 are the relative fitness (is equal to the average contribution to the
gene pool of the next generation that is made by individuals of the specified
genotype or phenotype) of homozygous A1, heterozygous (A1A2), and
homozygous A genotypes respectively

w the mean population relative fitness

Basic principles of response to selection

Since part of the observed differences between individuals is due to genetic effects,
phenotypic observations can be used to select animals. Genetic improvement can
be obtained without knowledge of the actual genes involved in the traits that need
to be improved. A breeding program works according to the following principles:
- Better phenotypes should have better genotypes
- If better genotypes are used as parents, the good genes are passed on to
offspring, hence offspring should have better genotypes as well (compared to
offspring of non- selected parents)
- Offspring with better genotypes can be expected to produce better
phenotypes
The conclusion is that if parents are selected based on phenotypic performance, we
can actually achieve improved performance over generations by improving the
average genetic value. The expected response to a selection program will depend on:
- The degree that selected parents were better than their generation average
- How accurately the parents were selected, and how much of their selection
superiority would be passed on to the next generation.
- How quickly the generation turnover is and how many selection rounds are
possible per time unit
Components of genetic gain and their interdependence
The expected amount of genetic improvement can be determined by quantifying the
following key factors:

Intensity of selection

This is a prediction of the superiority of selected group (in standard deviations).

Selection intensity depends on the proportion selected. We may expect that the
average of the very best animals (say top 5 %) to be higher (on average) than the
average of, for example, the best 50%.
Selection intensity is inversely related to the proportion selected.
- Note the trend, fewer selected ----- > more intensity

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 - Selection intensifies for males are mostly different from those of females.
Because of their higher reproductive rate, fewer males are needed in
breeding, and males have therefore usually higher selection intensity.
- When the size of the population selected from is small, selection intensities
are slightly reduced. For example, the best out of 10 will be not as good, on
average, as the best 10% of a 1000. The selection intensifies are 1.52 and
1.75, respectively.

Accuracy of selection

The accuracy of selection tells us how sure we are that a particular good animal
has also a good breeding value. With no information, the accuracy of an EBV is
zero, and with full information it is 1. Because most traits have heritability
considerably lower than 1, there is always error in estimating a breeding value from
phenotypic observations. Only a progeny test based on a large number of progeny
can give an almost perfect accurate EBV.
The accuracy of selection depends primarily on heritability of the trait selected. It is
equal to h (= square root of h2) if selection is based on individual phenotypic
performance only.
Selection based on repeated records on the same animal increases accuracy,
because the 'heritability' of a mean of repeated records is higher than that of single
records, the more so if repeatability is low. The reason is that in an average of
repeated records on one animal, the random environmental contributions are
averaged out, making the effect of the genotype more visible.
If information from relatives (parents, sibs or progeny) is used, the accuracy will
increase, the more so for traits with low heritability. Therefore, if family information
is used, it becomes less important whether or not a trait has a low heritability. The
expected increase in genetic gain may be up to 50% for low heritable traits. BLUP is
a genetic evaluation method that uses relatives' information. Notice that use of
relative's information is only possible if pedigree records are available.
Examples of accuracy
Information used Accuracy
h2 = 0.10 h2 = 0.30
Own information 0.32 0.55
Mean of 5 full sibs 0.32 0.48
Mean of 10 half sibs 0.23 0.33
1+2+3 0.43 0.65

Mean of 1000 half sibs 0.49 0.50

Mean of 1000 full sibs 0.70 0.71
Mean of 5 progeny 0.34 0.54
Mean of 10 progeny 0.45 0.67
Mean of 100 progeny 0.85 0.94

Generation interval

Generation Interval is defined as the average age of the parents when the progeny
are born. When generation intervals are short, the younger breeding animals will

Page 12 of 47
have a chance to express their genetic potential more quickly. This is desirable in
an on-going breeding programme when we may expect some genetic trend, and the
younger animals should be somewhat better than the older animals. Another way
of looking at it is that there will be selection rounds in a given time period if
younger breeding animals are used, and the genetic change per year will be higher.
An efficient breeding program therefore should try to keep the generation low.
A practical problem is often that from older breeding animals we can have more
certainty their genetic potential. Older animals have usually performed more often
(when traits are repeatedly expressed) or they may already have some offspring. In
other words, estimated breeding values from older animals are generally more
accurate. Therefore, we can imagine two alternative strategies: 1) selecting older
animals as breeding animals accurately, or 2) selecting younger animals less
accurately.
Generation interval usually differs between males and females. Since fewer males
are needed that females, males can be replaced earlier, so there is more potential to
have short generation intervals on the male side. Most young females usually need
to be retained as female reproductive rates are usually low (at least for sheep and
cattle). This leads not only to a low selection intensity on the female side, but also
to long generation intervals, as females are kept for the length of their productive
live.

Genetic variability

The best animals will stand out more if there is more variation in the trait
measured, i.e. they will relatively be more above the mean. The standard deviation
of a trait is multiplied by selection intensity to give the superiority of selected
parents in trait units.

ESTIMATION OF GENETIC CHANGE

Basic principles of response to selection

Since part of the observed differences between individuals is due to genetic effects,
phenotypic observations can be used to select animals. Genetic improvement can
be obtained without knowledge of the actual genes involved in the traits that need
to be improved. A breeding program works according to the following principles:
- Better phenotypes should have better genotypes
- If better genotypes are used as parents, the good genes are passed on to
offspring, hence offspring should have better genotypes as well (compared to
offspring of non- selected parents)
- Offspring with better genotypes can be expected to produce better
phenotypes
The conclusion is that if parents are selected based on phenotypic performance, we
can actually achieve improved performance over generations by improving the
average genetic value. The expected response to a selection program will depend on:

Page 13 of 47
 - The degree that selected parents were better than their generation average
- How accurately the parents were selected, and how much of their selection
superiority would be passed on to the next generation.
- How quickly the generation turnover is and how many selection rounds are
possible per time unit
Components of genetic gain and their interdependence
The expected amount of genetic improvement can be determined by quantifying the
following key factors:

Intensity of selection

This is a prediction of the superiority of selected group (in standard deviations).

Selection intensity depends on the proportion selected. We may expect that the
average of the very best animals (say top 5 %) to be higher (on average) than the
average of, for example, the best 50%.
Selection intensity is inversely related to the proportion selected.
- Note the trend, fewer selected ----- > more intensity
- Selection intensifies for males are mostly different from those of females.
Because of their higher reproductive rate, fewer males are needed in
breeding, and males have therefore usually higher selection intensity.
- When the size of the population selected from is small, selection intensities
are slightly reduced. For example, the best out of 10 will be not as good, on
average, as the best 10% of a 1000. The selection intensifies are 1.52 and
1.75, respectively.

Accuracy of selection

The accuracy of selection tells us how sure we are that a particular good animal
has also a good breeding value. With no information, the accuracy of an EBV is
zero, and with full information it is 1. Because most traits have heritability
considerably lower than 1, there is always error in estimating a breeding value from
phenotypic observations. Only a progeny test based on a large number of progeny
can give an almost perfect accurate EBV.
The accuracy of selection depends primarily on heritability of the trait selected. It is
equal to h (= square root of h2) if selection is based on individual phenotypic
performance only.
Selection based on repeated records on the same animal increases accuracy,
because the 'heritability' of a mean of repeated records is higher than that of single
records, the more so if repeatability is low. The reason is that in an average of
repeated records on one animal, the random environmental contributions are
averaged out, making the effect of the genotype more visible.
If information from relatives (parents, sibs or progeny) is used, the accuracy will
increase, the more so for traits with low heritability. Therefore, if family information

Page 14 of 47
is used, it becomes less important whether or not a trait has a low heritability. The
expected increase in genetic gain may be up to 50% for low heritable traits. BLUP is
a genetic evaluation method that uses relatives' information. Notice that use of
relative's information is only possible if pedigree records are available.
Examples of accuracy
Information used Accuracy
h2 = 0.10 h2 = 0.30
Own information 0.32 0.55
Mean of 5 full sibs 0.32 0.48
Mean of 10 half sibs 0.23 0.33
1+2+3 0.43 0.65

Mean of 1000 half sibs 0.49 0.50

Mean of 1000 full sibs 0.70 0.71
Mean of 5 progeny 0.34 0.54
Mean of 10 progeny 0.45 0.67
Mean of 100 progeny 0.85 0.94

Generation interval

Generation Interval is defined as the average age of the parents when the progeny
are born. When generation intervals are short, the younger breeding animals will
have a chance to express their genetic potential more quickly. This is desirable in
an on-going breeding programme when we may expect some genetic trend, and the
younger animals should be somewhat better than the older animals. Another way
of looking at it is that there will be selection rounds in a given time period if
younger breeding animals are used, and the genetic change per year will be higher.
An efficient breeding program therefore should try to keep the generation low.
A practical problem is often that from older breeding animals we can have more
certainty their genetic potential. Older animals have usually performed more often
(when traits are repeatedly expressed) or they may already have some offspring. In
other words, estimated breeding values from older animals are generally more
accurate. Therefore, we can imagine two alternative strategies: 1) selecting older
animals as breeding animals accurately, or 2) selecting younger animals less
accurately.
Generation interval usually differs between males and females. Since fewer males
are needed that females, males can be replaced earlier, so there is more potential to
have short generation intervals on the male side. Most young females usually need
to be retained as female reproductive rates are usually low (at least for sheep and
cattle). This leads not only to a low selection intensity on the female side, but also
to long generation intervals, as females are kept for the length of their productive
live.

Genetic variability

The best animals will stand out more if there is more variation in the trait
measured, i.e. they will relatively be more above the mean. The standard deviation
of a trait is multiplied by selection intensity to give the superiority of selected
parents in trait units.

Page 15 of 47
Predicting genetic merit of progeny

With the exception of the sex chromosomes, which we will ignore for the moment,
all animals carry two copies of every gene. One copy is inherited by random
sampling from the two copies carried by the male parent (sire) and the other copy is
inherited by random sampling from the two copies carried by the female parent
(dam). It follows that the additive genetic value of an individual, A i, can be
partitioned into three sources, such that,
1 1
Ai  As  A D  A m (1)
2 2
where AS and AD are the additive genetic values of the sire and dam and Am is the
Mendelian sampling contribution. The Mendelian sampling contribution reflects the
random selection of copies of parental genes. Since genes are inherited at random
from the parents, the average value Am over a large number of progeny is expected
to be zero.
It should be noted that equation 1 can be extended back so that the sire and dam
terms are replaced by their respective sire and dam terms (i.e. grandsire and grand
dam of individual i) and so on back through the ancestor pathways, e.g.

11 1  11 1 
Ai   ASS  ADS  A mS    A SD  A DD  A mD   A m (2)
22 2  22 2 
where SS is sire of the sire, DS is dam of the sire, etc., and AmS and AmD are the sire
and dam Mendelian sampling terms.

Response per generation

Response to selection is defined as the difference between the mean genetic (value
of progeny of selected parents and the mean genetic value of progeny of all possible
parents. Genetic improvement per generation will depend on two factors: How good
were the selected parents (selection differential - superiority of the selected parents
- the difference between the average of the selected group and the average of the
group they were selected from) and how much of this superiority is transferred to
the next generation. The response to selection is obtained by:

R  i.rIA A (3)

Remember that response to selection based on own phenotypes was calculated as

R  i.h2 P . As the accuracy for such selection is rIA  h and as h2 P (phenotypic
standard deviation)   A (genetic standard deviation), this turns out to be the same
as R  i.h2 P . R  i.rIA A is a general formula for the response to selection which
applies whenever the value on which animals are selected, I, is linearly related to
their additive genetic value.

Page 16 of 47
Response per unit time

Response defined in equation (7) is the response from one generation to the next. If
conditions remain constant over generations, it is also the response per generation.
Generation intervals vary widely across species. For example, a generation interval
for poultry and swine can be as short as 1 year, whereas for progeny tEs4ing
schemes in cattle, generation interval is often 7 years or more. Generation intervals
can also be altered within species by changing the age at which animals are
selected and bred. In general, it is more useful to estimate response per unit time,
and usually response on per year basis. Response per year is often given the
notation as response per generation, R, (In this case response per year is obtained
by dividing equation (7) by the generation interval, L, to get
i.rIA A
R (4)
L
(Note, in general, as here, we must be careful to know whether response, R, is
expressed per generation, per year or in some other unit of time). Generation
interval is defined as the time between the birth of the parent and the birth of it's
progeny. Equation 7 holds the key to designing breeding programs. Response per
unit time is proportional to the intensity of selection, the accuracy of genetic
evaluation and the square root of the genetic variance and is inversely proportional
to the generation interval.

Multiple Pathways of Selection

In practise, selection intensity and selection accuracy are usually not the same for
male and females. A more appropriate formula to predict selection response is
therefore:
isires .rIAsires  idams .rIAdams
R yr  A (5)
L sires  L dams

In most species, males have a higher reproductive rate than females, thus we need
fewer males for breeding and consequently can have a higher intensity of selection
in males than females. In some species, traits of interest are recorded only in one
sex, examples being milk yield in dairy cattle, litter size in swine and rate of egg
production in poultry. This can lead to different accuracies of evaluation in the two
sexes, since one sex has it's own performance contributing to it's evaluation while
in the other sex genetic evaluation must be based entirely on information from
relatives. Similarly, different sexes can have different generation intervals for a
variety of reasons, e.g. the sex with the highest reproductive rate (usually males)
may take less time to produce replacement offspring and hence potentially have the
shortest generation interval.
Rendel and Robertson (1950) and Robertson and Rendel (1950) pointed out that in
any breeding program there are actually four basic (pathways of genetic
improvement, corresponding to the total of four sources of parental genes of male
and female progeny. These four pathways are:

Page 17 of 47
 male parents of male progeny (sires of sons - SS)
female parents of male progeny (dam of sons - DS)
male parents of female progeny (sires of daughters - SD)
female parents of female progeny (dam of daughters - DD).
Robertson and Rendel showed that where each of the four pathways of genetic
improvement were separately recognized, response per generation as predicted by
equation (8) could be rewritten as:
number of paths

 int ensity  accuracy   A

R i 1
number of paths
(6)

i 1
Generation Interval

iSS.rSS  iDS.rDS  iSD.rSD  iDD.rDD

 A (7)
L SS  L DS  L SD  L DD

This formula can be adjusted for use in breeding programmes, which apply testing
of young unproven males (YB) in the population, replacing use of proven males (SD)

iSS.rIASS  iDS.rDS  1  y  iSD.rSD   y  i YB.rYB  iDD.rDD

 A (8)
L SS  L DS  L SD  L DD
where y = proportional use of young males on dams. It is assumed, that these
young males do not add genetic superiority by selection when selection is SS and
DS is already considered. However, they have their specific generation interval,
usually shorter that LSD.
We should note that equation (6) predicts "steady state" rate of response to
selection. In practice it may take several generations to approach this steady state
and in some cases a true steady state may never be reached. It is therefore
generally safer to think of R predicted by equation
Error! Reference source not found. as the prediction of average rate of response
per year, recognizing that predicted response may well vary from one year to the
next. Even where a steady state response rate is eventually achieved, genetic
response will usually be variable from one year to the next in the early generations
of the breeding program. This problem is solved by using the Gene-flow method
and is discussed in more detail by Hill (1974). Gene flow can be thought of in terms
of the proportion of genes in a group of animal originating from a base group of
selected individuals or in terms of a proportion of the additive genetic merit of the
base group to be expressed in a progeny group.
To illustrate a breeding program in which all four pathways of improvement are
recognized, we can consider a conventional progeny testing program for
improvement of milk production in dairy cattle with the use of artificial
insemination. For simplicity we assume all cows reproduce naturally without the
aid of embryo transfer. In such a scheme, young bulls are tested by mating to a
(hopefully) random sample of cows, the resulting heifers are reared and their first

Page 18 of 47
lactation performance is recorded. This daughter lactation information is then used
to produce a genetic evaluation on each young bull, often referred to as the "first
proof' of a bull. At this stage the best bulls can be selected for breeding and the
remainder discarded. In contrast, heifers and cows are evaluated largely based on
their own lactation performance. In a population of several hundred thousand
recorded dairy cows, several hundred young bulls, perhaps up to a thousand,
would be needed each generation.
We can now consider each of the four pathways of genetic improvement in a highly
efficient hypothetical progeny testing program.
Sires of sons (SS): Since we only test a few hundred young bulls, and every sire
can produce tens of thousands of doses of semen, we need only a few sires to
produce these young bulls each generation. Thus we need to select only the top 1
or 2% of tested bulls as sires of sons. These sires have high accuracy of genetic
evaluation, since progeny tests generally give high accuracy. The generation
interval will, however, be at least 6 years because of the time taken from birth of
the young bull, through the birth of his first crop of test daughters, through their
first lactation to the birth of his sons.
Sires of daughters (SD): Since there are several hundred thousand cows to be
bred, many more bulls are required to produce the necessary amount of semen
each generation. In an efficient scheme, the top 10-15% of young bulls can be
selected, giving lower selection intensity than for sires of sons. Accuracy of
selection is the same as for sires of sons because they are chosen on the basis of
the same information. The generation interval is, however, about a year longer
because it takes time to breed a large population of cows and the better bulls will
be used by farmers for a little longer than the not so good bulls.
Dams of sons (DS): Since there are several hundred thousand cows and only a few
hundred sons are tested, dams of sons can be selected very intensely, perhaps only
the best 0.1 to 0.5% being required. But evaluation is based on their own
performance, which has lower accuracy than a progeny test. These cows could be
bred in their second lactation boned on their first lactation performance and part of
their second lactation performance, so that they would be around 4 1/2 to 5 years
old at the birth of their sons.
Dams of daughters (DD): Dairy cows have a very low reproductive rate, producing
less than one live calf per year after allowing for average calving intervals and
mortality of foetuses and calves. Allowing for disease and other losses of growing
heifers and for the fact that only half the calves are females, only about 1 in 3
calvings result in a potential replacement heifer for the dairy herd. Since average
life in the herd in many countries (Kenya excluded) is often not much over three
lactations, the average cow barely has sufficient time to produce a replacement
before she leaves the herd. There is thus very little room for selection of dams of
cows, with perhaps 90% of all cows required for breeding. Accuracy of selection
would be very similar to that for dams of sires. However, generation interval is
generally increased by a year or two since the average cow takes close to three
carvings to produce a replacement.

Page 19 of 47
The parameters applying to each pathway are summarized in the table below
Pathway Proportion selected Intensity Accuracy Generation
(p - %) (i) (rIA) interval (L)
Sires of sons 2 2.42 0.90 6
Sires of daughters 10 1.75 0.90 7
Dams of sons 0.5 2.89 0.60 5
Dams of daughters 90 0.19 0.60 6

These parameters (assuming that the genetic variance is the same for all pathways)
can be used to obtain an estimated rate of response for this breeding programme
as:
2.42  0.9  1.75  0.9  2.89  0.6  0.19  0.6
R A
6756
R  0.233 A per year

Response could be expressed in many units, but the three most common and
probably most useful are  A per year, absolute units per year (e.g. kg milk per
year) or as a percentage of the mean per year.
Imagine that the dairy cattle population above has a mean yield of 6000 kg, that
the heritability of milk yield is 0.25 and that the coefficient of variation (CV) is 0.18.
Since

 2  h2p2
A

And

 
2
 2  CV  X
P

Then

 
2
  0.18  6000 
2
 2  CV  X
P

Hence

 2  0.25 1080 
2
A

And

 A  540
Hence R = 0.233 x 540 = 125.82 kg per year or alternatively R = 125.82/6000 =
2.1% per year.
The choice of units will depend on how the results are to be used. Use of genetic
standard deviation units may be useful to geneticists who think in such terms and
allow results to be readily converted from one population to the next if it is believed
that the major variation between populations is in the absolute amount of genetic
variance. For example, this would be true if h2 and CV were the same for different
population but the mean level of performance differed.

Page 20 of 47
The prediction of rate of response to selection given by equation
Error! Reference source not found. and in its more complete form by equation
Error! Reference source not found. holds the key to understanding many of the
basic principles of design of breeding programmes.

SELECTION METHODS

Single trait selection

Individual or mass selection

Essentially the procedure involves measuring or performance testing the potential

parent individuals and then selecting as parents those with records closest to the
objective. Individual selection is appropriate for characters with heritability in the
range approximately 0.25-1.0 and which can easily be measured in the potential
parents. For various reasons this type of selection is not always the most
appropriate and therefore in many cases other selection methods are used.

Pedigree selection

Pedigree refers to the recording of parentage so that the genetic relationships

between individuals in a population are known. Such recording is required for all
the selection methods apart from individual or mass selection. For characters
which can only be measured in the female it is common practice to undertake an
initial selection of males at a young age on the basis of female ancestor
performance and later to supplement this with family selection or progeny testing.

Family selection

Whole families are selected or rejected as units, according to the mean phenotypic
value of the family. Individual values are thus not acted on except in so far as they
determine the family mean. By definition when the heritability of a character is in
the range 0 to approximately 0.25 the phenotype of an individual is a relatively
inaccurate estimate of its breeding value. Then it is more efficient in terms of
genetic gain per unit time to base selection on family performance rather than on
individual performance.
Selection by progeny testing
For characters of low heritability and for characters, which can only be measured
in one sex or after slaughter, selection on the basis of progeny performance is
appropriate, and more efficient than other forms of selection.

Correlated characters

So far selection has been described in terms of a single character. Many characters
are genetically correlated as a consequence of pleitropy and linkage. This selection
on one character leads to correlated changes in other characters. The relationship
can be quantified in the form of a genetic correlation whose value is within the
limits +1 to -1. From the value of the genetic correlation it is possible to predict the

Page 21 of 47
correlation changes, which may occur in a second character as a consequence of
selection on the primary character.
CR2  i1h1 A2 rG and R2  i 2 h2 A2
where
CR 2 = indirect (correlated) response in character 2 as a result of selection
on character 1.
R2 = direct response in character 2 to selection for character 2

h12 and h22 = heritability of characters 1 and 2 respectively.

rG = genetic correlation between characters 1 and 2

 2A2 = additive genetic variance for character 2

i1 and i 2 = selection intensity for character 1 and 2 respectively.

Multiple traits selection

Tandem selection
This procedure is the simplest and involves selecting for one character for several
generations and then changing the objective of selection and selecting for the
second character for a further several generations.
Selection by means of independent culling levels
This procedure involves setting independent thresholds or culling levels for each
character to be selected and choosing as parent those individual whose
performance is above or appropriately below the culling levels.
Selection index
At its simplest this method involves combining the measurements of two or more
characters into a single value for each individual. To obtain the maximum rate of
change for all characters under selection simultaneously it is necessary to combine
the phenotypic values for each character of an individual in such a way as to
account for the relative economic values, the heritabilities, the phenotypic
variances and the genetic correlation of the characters. Selection indexes are used
widely but do require access to relatively sophisticated computation facilities.

Selection index methodology

At its simplest this method involves combining the measurements of two or more
characters into a single value for each individual. To obtain the maximum rate of
change for all characters under selection simultaneously it is necessary to combine
the phenotypic values for each character of an individual in such a way as to
account for the relative economic values, the heritabilities, the phenotypic
variances and the genetic correlation of the characters.

Page 22 of 47
Hazel (1943) developed a method of combining information from several sources, be
it the same trait from different types of relatives or different traits measured on the
animal, to predict the animal's genetic merit. The method for establishing a
selection criterion for a given selection objective is referred to as selection index
methodology.
Selection index theory was developed to estimate the genetic value of an animal,
using observations for particular traits. We distinguish
- the true genetic value of an animal: this value is unknown and not
measurable. One could think of a genetic value for one trait or a
combination of traits (an aggregate genotype, H). In the latter case, every
trait is weighted with its relative importance (for instance: relative economic
value) - selection objective (breeding objective)
- the index value I of an animal: this is an estimate of the true genetic value of
an animal. A number of observations or information sources are weighted in
an index, such that the genetic value will be estimated with the highest
accuracy possible.
If one is interested in the breeding value for one trait (the breeding goal is one trait),
the following sources of information can be used:
(a). One or more observations for that specific trait on the animal itself,
(b). Observations for that specific trait on relatives,
(c). Observations for other traits on the animal itself and/or relatives,
Observations from relatives can be useful when
- the number of observations on the animal itself is limited: higher accuracy of
the estimated breeding value can be accomplished using information on
relatives,
- traits in the breeding goal cannot be observed on the animal itself (e.g. milk
production for sires)
Observations on correlated traits are useful when
- they add additional information (increase accuracy),
- they are easier to measure than the trait in the breeding goal.
Selection index is able to combine these different sources of information to estimate
the true genetic value. The selection index can be presented as follows:

I  b1x1  b2x 2  ...  bn x n

where,
I = index value - the estimation of the genetic value for the aggregate genotype,
Xi = ith source of information - one observation or an average of several
observations, expressed as a deviation from a conditional mean,
bi = weighting factor for the ith source of information

Page 23 of 47
If several assumptions are fulfilled, selection index will be an optimum procedure.
These assumptions include linearity of economic values, normally distributed
observations and genetic values, conditional means (i.e. fixed effects such as herd
or season) known without error (unbiasedness). Given these assumptions, the
selection index is the best estimator of genetic value, because it fulfils the following
criteria
- it maximizes the correlation between true genetic value for the aggregate
genotype (H) and the estimated genetic value (index value I),
- it maximizes the probability of a correct ranking of the animals according to
their true breeding value
- it maximizes genetic gain by selection,
- it minimizes the differences between true breeding value and estimated
breeding value.
The selection index procedure is also called the 'Best Linear Prediction' (BLP) of the
breeding value. In the next topic on breeding value estimation, mixed model
methods are presented which do not make the assumption that fixed effect means
are known without error. In those methods breeding values and fixed effects are
estimated simultaneously which results in unbiased breeding values (BLUP). For
estimating breeding values these so-called BLUP methods are to be preferred. For
prediction of response to selection, however, selection index methods are very
helpful
Essential point in estimating breeding values, using the selection index, is the
derivation of weighting factors (b-values) for the information sources that fulfil the
above mentioned criteria. Next to that, one can derive the accuracy of the estimated
breeding value and predict the relative change in the different traits in the breeding
goal (selection objective). Selection index theory is very flexible and offers great
opportunities in predicting accuracy of estimating breeding values. Consequently it
plays an important role in prediction of genetic response of breeding schemes and
the design of breeding schemes.

Selection objective and selection criterion

Selection objective

Assume that there are several traits which have to be improved, denoted by Y1, Y2, .
Yn and the traits have economic values of a1, a2, ..., an, respectively.
The economic value of a trait represents the additional economic return per
marginal unit improvement in the trait. For example, the economic value of carcass
lean content may be two economic units, assuming no change in food intake or
growth rate.
Since the economic objective is to improve all traits, then the traits and their
economic values are combined into a selection objective, where selection objective

H= I  a1Y1  a 2 Y2  ...  a n Yn

Page 24 of 47
The selection objective can be expressed in matrix notation as a'Y and is denoted
by H.
For example, if growth rate and carcass lean content are to be improved and have
economic values of 5 and 2, respectively, then the selection objective is
5 x growth rate + 2 x carcass lean content.

Selection criteria

The traits which are measured to predict the breeding value are denoted X1. X2, ...,
Xm. The measured traits are combined into an index on which the animals are
selected. The selection index or selection criterion is follows:

I  b1x1  b2x 2  ...  bn x n

The selection criterion can be expressed in matrix notation as b'X and is denoted I
(for index).
For example, if growth rate and ultrasonic backfat depth are the traits measured,
then animals are selected on the basis of
b1 x growth rate + b2 x ultrasonic backfat depth.
Traits in the selection criterion need not necessarily be the same traits in the
selection objective. For example, in one breeding programme, growth rate and
carcass lean content are the traits to be improved, while growth rate and ultrasonic
backfat depth are the traits measured for selection purposes. Similarly, the number
of traits in the selection objective need not be the same as the number of traits in
the selection criterion. In a second breeding programme, litter weight at weaning is
to be improved, and the selection criterion consists of litter size and weight at birth.
The selection index method determines the selection criterion coefficients that
maximise the response in the selection objective, H, with selection on the selection
criterion, I. As several traits can be included in both the selection objective and the
selection criterion, then information on the variance of the traits and on
relationship between the traits at the phenotypic and genetic levels is required. The
information is in the form of three matrices:
P: the phenotypic variance-covariance matrix of traits in the selection criterion.
G: the genetic covariance matrix between traits in the selection objective and the
traits in the selection criterion
C: the genetic variance-covariance matrix of traits in the selection objective.

Page 25 of 47
GENETIC PARAMETERS

Heritability

1. Heritability in the broad sense (H2): is the proportion of the phenotypic

variance that is due to genetic effects including additive, dominance and epistasis:
VG VA  VD  VI
H2  
VP VP

It measures the strength of the relationship between the phenotypic values for a
trait and the genotypic values.
2. Heritability in the narrow sense (h2): is the proportion of the phenotypic
variance that is due to additive genetic effects only.

2 VA VA
h  
VP VA  VD  VI  VEP  VET

What does the heritability in the narrow sense measure?

1. The strength of the relationship between the phenotypic values and the
breeding values for a trait in the population. Therefore, it can be viewed as
the coefficient of regression of the breeding value on the phenotypic value.
2. It measures the degree to which the offspring resemble their parents in
performance for a trait:
- If a trait has a large heritability: animals with high performance for the trait
will produce offspring with high performance.
- If a trait has a small heritability: performance records of parents reveal little
information about the performance of their offspring.
• Heritability is always positive ranging from 0 to 1.0.
• Traits with low heritability (h2< 0.20):
– reproductive traits like days open calving interval, litter size, and
conception rate
– longevity or productive live (about 0.10)
– weaning weight in swine (about 0.10)
• Moderately heritable traits (h2 of 0.2 to 0.4):
– Milk yield, fat yield and protein yield (0.25-0.35)
– Birth weight in sheep
– Yearling weight in sheep
• Highly heritable traits (h2> 0.4):
– Carcass traits and traits related to skeletal dimensions like mature
body weight

Page 26 of 47
 – Fat and protein% in milk.
• Notes on heritability:
• Heritability is a population measure not a value associated with a single
individual.
• Heritability of a trait varies from one population to another and from
environment to another.

Importance of heritability

1. Heritability is important in selection: The accuracy of selection is higher for a

highly heritable trait than a low heritable trait. The larger the accuracy of
selection, the larger is the expected response due to selection.
With selection based on phenotypic values:
• Large h2high accuracy of selection (phenotypic value is a good indicator of
breeding value)
• Small h2low accuracy of selection (phenotypic value is not a good
indicator of breeding value)
2. Heritability is important in prediction of breeding values and producing
ability.
• Prediction of BV of animal i based on its phenotypic value, Pi ,is obtained as:

BVi  h2 (Pi  P)

3. Heritability is important in management:

- Large h2genetic factors have important role as in growth traits (performance
can be improved by selection).
- Small h2environmental factors are important as in reproductive traits
(selection is less effective and performance is improved mainly by improving the
environmental effects such as improving nutrition and management practices).
Examples of heritability estimates
Beef cattle h2 Dairy cattle h2 Layers h2 Broilers h2
Birth weight .35 Milk yield .25 Chick .05 7-week weight .45
livability
Weaning .30 Fat yield .25 Adult .10 Feed .70
weight livability consumption
Weaning .25 Solids-not- .25 Body depth .25 Feed .35
score fat yield conversion
Feedlot gain .45 Protein yield .25 Adult body .55 Breast fleshing .10
weight
Carcass .40 Fat % .50 Egg .15 Fat deposition .50
grade production
Fat thickness .33 Protein % .50 Egg weight .55
Rib eye area .58 Fertility .05
Marbling .42
Retail .30
product %
Calving .08

Page 27 of 47
interval
Conception .05
rate

Repeatability

Repeatability (r) is the proportion of the phenotypic variance that is due to

permanent effects (genetic effects and permanent environmental effects):
VA  VD  VI  VEP
r
VP

What does the repeatability measure?

1. The strength of the relationship between repeated records. Therefore,

repeatability can be estimated as the correlation between repeated records on
the same animals.
2. The strength of the relationship between single performance records and
producing ability (permanent effects). Therefore, repeatability can be viewed as
the regression of PA on the phenotype.
Importance of repeatability
1. It is useful in prediction of producing ability and therefore the animal’s next
record from the current and previous records:
- If ris high, we can predict the animal’s next record more accurately
- If r is low then the prediction of the next record has low accuracy.
To predict the producing ability (most probable producing ability, MPPA) from n
previous records:

ˆ  nr
MPPA  PA (Pi  P)
1  (n  1)r

Pi = the average of the n records of the animal i

P = is the mean for all animals

Example: suppose a cow has three milk records: 4000kg in the first record, 5000
kg in the second, and 6000 kg in the third. Suppose also that the mean of all cows
is 4600 kg and the repeatability of milk yield is 0.60, then the predicted producing
ability of this cow is:

ˆi  (3)(0.60)
PA (5000  4600)  327kg
1  (3  1)(0.60)

ˆi  P  PA
P ˆ  4600  327  4927kg

2. Repeatability is important in prediction of breeding values from multiple

records on the same animals:

Page 28 of 47
 ˆi  nh2
BV (Pi  P)
1  (n  1)r

For the previous example if heritability of milk yield in this population is 0.25 then

ˆi  (3)(0.25)
BV (5000  4600)  204.6kg
1  (3  1)(0.60)
3. Repeatability is important in making culling decisions:
When r is high we can cull animals of poor performance on the basis of the first
record
When r is low one should wait for more records before making a culling decision
on the animal
Examples of Repeatability Estimates
Beef cattle r Dairy cattle r Poultry r Sheep r
Calving date .35 Services per .15 Egg weight .90 Number born 0.15
(dam) conception
Birth weight .20 Calving .15 Egg shape .95 Birth weight 0.35
(dam) interval (dam)
Weaning weight .40 Milk yield .50 Shell .65 60-day 0.25
(dam) thickness weaning
weight (dam)
Body .80 % Fat .60 Fleece grade 0.60
measurements
Teat .55
placement

Genetic correlation

So far selection has been described in terms of a single character. Many characters
are genetically correlated as a consequence of pleitropy and linkage. This selection
on one character leads to correlated changes in other characters. The relationship
can be quantified in the form of a genetic correlation whose value is within the
limits +1 to -1. From the value of the genetic correlation it is possible to predict the
correlation changes, which may occur in a second character as a consequence of
selection on the primary character.
CR2  i1h1 A2rG and R2  i2h2 A2

where
CR2 = indirect (correlated) response in character 2 as a result of selection
on character 1.
R2 = direct response in character 2 to selection for character 2

h12 and h22 = heritability of characters 1 and 2 respectively.

rG = genetic correlation between characters 1 and 2

 A2
2
= additive genetic variance for character 2

Page 29 of 47
i1 and i2 = selection intensity for character 1 and 2 respectively.

Page 30 of 47
MATING SYSTEMS

Inbreeding

Inbreeding depression and heterozygosity are closely connected to the mating

systems occurring as a result either from inbreeding or from crossing. Increasing
genetic variability and vigour after crossing and decreasing genetic variability and
vitality after inbreeding are different expressions of basically the same
phenomenon.
Inbreeding means mating of related animals, which also means that the amount of
relation is closer in these animals than among random individuals of the
population. Mating of relatives increases genetic similarity, because parental genes
being identical by descent and having the same effects concur after fertilisation.
Inbreeding leads to a reduction of unequal allelic pairs. Inbreeding depression in
general is expected to be followed by decreasing vitality, declining of adaptability
and fertility traits and growth. Increasing probability of losing favourable alleles
due to a decline in heterozygosity by mating relatives. Thus, inbreeding can reduce
the commercial exploitation in dairy herds.

Measuring inbreeding
If an individual mate with a relative, offspring may be homozygous for an allele
which is identical by descent from one of the ancestors.
There are three levels in which inbreeding can be estimated: population level,
individual level, and locus level. The less specific inbreeding estimator is the
population average, which applies to all individuals and loci. The mean population
inbreeding can be readily calculated as:

Ft  1  1  Ft

Where Ft is the inbreeding at a time (generation) t, ΔF = 1/2N is the rate of increase

of inbreeding per generation, and N is the effective population size. This formula
assumes a fixed population size, discrete generations, and neither migration nor
mutation.
The next level of specificity is inbreeding at individual level. This inbreeding
coefficient (F) is depends on a specific pedigree and therefore it can differ across
individuals, although it is the same across all loci within an individual.
n1 n21
1
Fk     1  FA ,
2
With Fk - inbreeding coefficient of individual k, n1 (n2) is the number of paternal
(maternal) generations between the parents of k and common ancestor, F -
A
inbreeding coefficient of inbred ancestor and ∑ notes the summation in case of
multiple inbreeding due to the same or several common ancestors.

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Crossbreeding

Crossbreeding involves the mating of two or more breeds. Methods of systematic

crossbreeding include terminal sire crossing, of which there are several variations
including recreation of the F1 in each generation, three way crossing, rotational
crossing and combinations of terminal crossing and rotational crossing.
Cunningham and Syrstad (1987) categorised crossbreeding strategies into three: -
- Breed replacement strategies
- Establishment of stable crossbreeding systems
- Formation of synthetic populations.
The theoretical consequences of crossbreeding were documented by Willham
(1970).
Genetic basis of crossbreeding
The genetic basis of crossbreeding can broadly be classified into two: additive and
non-additive.

Additive inheritance

1. Additive component. This is that which is due to the averaging of merit in the
parental lines or breeds, with simple weighting according to level of gene
representation of each parental breed in the crossbred genotype. Additive genetic
effects originate from contributions of single loci and are therefore transferred
directly from parent to progeny.

This additive component can be divided into the individual and maternal additive
genetic effects. The individual additive genetic effect is the contribution to offspring
phenotype attributable to its own set of genes. Maternal additive genetic effects are
defined as any contribution or influence on the offspring's phenotype attributable
to its own dam. Maternal effects can be classified into prenatal (e.g. cytoplasm of
the egg and uterine environment) and postnatal (e.g. milk production, method of
rearing and/or mothering ability).

Heterosis

2. Non additive component which we observe as heterosis: It is the amount by which

merit in crossbreds deviates from the additive component. Heterosis is usually
attributed to genetic interactions within loci (dominance) and interaction between
loci (epistasis). Heterotic effects can be classified into individual and maternal
heterosis. Individual heterosis is the deviation (or superiority) in performance in an
individual relative to the average of the parental breeds, with maternal, parental or
sex-linked effects playing no role. Maternal heterosis refers to heterosis in the
population attributed to using crossbred instead of purebred dams and occurs due
to the dam itself possessing heterosis.

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The genetic basis of heterosis

The genetic basis of heterosis can be divided into two components

Dominance
The level of allelic heterozygosity is expected to increase where an individual’s
parents come from two different breeds. This is because the individual’s genes are
sampled from two breeds, which differ in their gene frequency. Crossbreeding leads
to an increase of the frequency of heterozygous loci, which better equip the
individual to perform well under varying or stressful environments. It is normally
assumed that a linear relationship exists between dominance and the degree of
heterozygosity. Dominance is therefore expected to be favourable. Dominance
genetic effects (or deviations) originate from interactions between alleles at the
same loci at the two homologous chromosomes. Dominance deviations in parents
are broken down during gametogenesis and new deviations are created in the
offspring.

For a single locus, the dominance deviation is indicated by 'd'

Genotype value
A1A1 a
A1A2 d
A2A2 -a
With d= 0, there is in fact no dominance; with 0 > d> a, one talks of partial
dominance; d = a gives complete dominance, in other words A2 is a recessive allele;
and finally d > a gives over-dominance. With d > 0, heterozygous animals give a
relative advantage. Inbreeding gives shortage of heterozygous animals, thus giving
rise to inbreeding depression for genes with positive dominance deviations.
Epistasis
To carry out respective tasks, genes must co-operate well. In purebred animals,
this has been made possible by several generations of selection. During
crossbreeding, genes must interact with other genes from other loci that are derived
from different breeds. Crossbred animals may therefore be out of harmony with
themselves. As opposed to dominance, epistasis will have a negative effect in
crossbred animals.
Epistatic genetic effects (or deviation) originate from interaction of alleles at
different loci either at the same chromosome or the homologous chromosome or
even at other chromosomes.

Epistatic deviations originating from interactions between alleles at different loci

behave like dominance deviations and are not transferred from parents to offspring.
However, epistatic deviations originating from linked loci (located together closely at
the same chromosome) have a probability of transfer to progeny that is higher than

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one half. Long-term selection in a population will favour combinations of alleles
that have a positive epistatic effect.

The value of crossbreeding

The value of crossbreeding can be split into a number of components:

1. The averaging of breed effects - This component is often of negative value where
parental breed is superior for the environment of interest. However, in many cases
the best type of animal is intermediate in some way, for example (i) an animal of
intermediate body size may best be suited to the length of the grazing season, or to
market demands and, ii) where two traits are negatively correlated across breeds,
and where the, multiplicatively in the expression of economic value. This is the
case in many Bos taurus (fast growing) x Bos indicus (stress resistant) crosses.
2. Direct heterosis: Crossbred individuals often exhibit heterosis. Heterosis is
measured as the extra performance of the crossbreeds over the weighted average of
their parent breeds. The percentage increase in performance ranges about 0 - 10
for growth traits and 5 - 25% for fertility traits. The effect of heterosis on the total
production can be even more than this, as effects accumulate over traits.
3. Maternal heterosis: Crossbred dams can exhibit considerable heterosis in their
ability to raise many, fast growing, and viable offspring.
4. Sire-Dam complementation: The cost of maintaining breeding females can be very
high, especially where fecundity is low. A good crossbreeding system aims to use
breeding females, which are small (but not so small for dystocia to be a problem) as
well as prolific. When a large breed of sire is used the proportion of feed directed to
growing animals is increased, and the production system benefits accordingly.
5. Possibly cheap source of breeding animals: This is evident in the Australian
prime lamb industry, where surplus Merino ewes are bought cheaply and crossed
with Border Leicester ram to produce prime lamb dams. These dams show
considerable heterosis for weaning rate, and yet retain reasonable growth
characteristics. However, much of the economic value of this crossing system is
due to the cheap source of Merino ewes.
6. Potentially widest use of genetic resources. The range of genetic material available
is greater when more than one breed is involved, and this can give greater flexibility
in identifying and developing an efficient breeding programme. Selection intensities
can be higher and inbreeding problem can be largely avoided.

Systems of breed utilization (mating systems)

Systems of breed utilization are listed in the table below. Terminal crossing is most
commonly called static crossbreeding in contrast to rotational systems. Terminal
crossings have in common that the offspring at the "terminal" stage are not used
for replacements. Terminal crossing is mainly applied in meat production.
Mating system Symbol
Production with one population
Purebreeding

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Synthetics; two breeds, balanced (AB)Syn
Synthetics; three breeds, balanced (ABC)Syn
Terminal crossbreeding
Two-way cross B*A
Three-way cross C*BA
Four-way cross CD*BA
Backcross B*BA
Rotational crossbreeding
Two breeds; balanced (AB)Rot
Three breeds; balanced (ABC)Rot
Terminal rotation C*(AB)Rot
Two breeds; breed preference (AAB)Rot
Two breeds; generation preference (AB)Rot

Rotational systems

There are many modifications of the rotational crossbreeding systems.

Terminal rotation: when rotational matings are restricted to produce only crossbred
dams which are later mated to terminal sires to get a final generation of slaughter
animals.
Rotational system with generation preference: Assume that in a criss-cross rotation
breed A is more prolific and breed B is a better meat breed. Dams of the more
prolific generation (2/3 genes of A) are then kept in a larger proportion than dams
from the less productive one. The consequence is that the proportion of slaughter
animals of the generation with better meat performance is also higher (2/3 genes of
breed B).
Rotation with breed preference: Whenever there are differences in the overall
performance of the breeds involved in a rotation, another question arises. Why
should the sequence of using sires of each breed be equal? Or would it be beneficial
to use the better breed more often to have a higher average gene percentage of it in
the system? This would certainly mean an improvement due to breed difference at
the expense of a reduction in heterosis. When the criss-cross rotation ABRot is
substituted by AABRot (i.e. 2/3 of A gene in the system), the heterosis expressed is
4/7 instead of 2/3. Whenever the overall breed difference is higher than 4/7 of the
overall heterosis between the two breeds, this "rotation with breed preference"
would be more efficient than the conventional crisscross system.
Sire rotation without dam identification: So far modified systems of rotation have
been discussed under the assumption of controlled matings. These assumptions
hold usually for pig production and for any commercial use of artificial
insemination. In beef cattle herds and sheep flocks, however, there is usually
multiple sire joining i.e. in each mating period only sires of one breed should be
used. Such a sire rotation, regardless of the genotype of dams they mate, ends up
in more heterosis than a synthetic breed with equal gene proportions. The
superiority depends mainly on the age structure of the dams. The higher the
proportion of younger dams the higher the effect of heterosis through rotation.
Sire rotation with dam identification: There is another alternative procedure of sire
rotation under non-controlled matings. Whereas controlled matings might be
impracticable in the commercial sheep and beef industry, a simple dam

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identification according to its crossbred genotype may be easy to handle. This is
obtained by identifying female lamb or calves by notches according to their dams'
notch identifying the crossbred generation. In a criss-cross system this may be for
example one notch for 1/3 gene proportion of breed A and two for 2/3 gene
proportion. Let us again assume that only one sire breed has been used in a
mating period, say breed A. Mated to these two genotypes of females, those dams
with 1/3 gene proportion of A will give birth to offspring with 2/3 and the other will
produce offspring with 5/6 gene percentage of breed A. The first are notched as
potential replacements, the second will be considered inferior and determined for
slaughter. In the next mating period sires of breed B will be mated which in turn
produce potential replacements (1/3 gene percentage of breed A, one notch) and an
inferior genotype (1/6 gene proportion of A).

Development of synthetic breeds

The decision to invest in synthetic breed formation in specific situations must be

based on consideration of the natural environment (average temperatures, rainfall
amount and distribution, solar radiation, relative humidity, soil fertility and the
quality of natural forages), the level of management, which is economically feasible,
and the relative importance of milk and beef as production goals. Medium and long-
term projection of input costs and output values are needed because the development
of synthetic breeds has both intermediate and long term implications (Trail and
Gregory, 1981). The steps to be followed are as follows:
Step 1: Decide on the number of breeds and choose those that have complementary
effects (breeding values) for the traits of interest to produce the biological type of cattle
desired. The choice of the foundation breeds to be used in the formation of synthetic
breeds should be based on performance under environmental conditions similar to
those in which the synthetic breed will be maintained. Inclusion of the maximum
number of breeds of demonstrated potential in the synthetic would result in the
retention of the highest proportion of the original heterosis.
Step 2: Decide on the percentage of each breed chosen that you need in the synthetic
to obtain the desired biological type. This is determined by the degree of
environmental modification. The percentage of heterozygosity retained is high when
the contribution of the foundation breeds in the synthetic breed is equal. Hence it is
desirable to have equal contributions by each breed if this can be done while at the
same time achieving optimum additive genetic composition. For example, a synthetic
breed based on equal contributions by four breeds, the proportion of mean F1
heterozygosity would be
1 - [(.25)2 + (.25)2 + (.25)2 + (.25)2] = 0.75
A synthetic breed based on unequal contributions by four breeds would retain a lower
proportion of mean F1 heterozygosity, or
1 - [(.375)2 + (.375)2 + (.125)2 + (.125)2] = 0.688
Step 3: Decide a mating scheme to reach the desired percentage of each breed in the
shortest period of time. Crossbred bulls are always needed

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Step 4: Keep good records (both biological and economic) on the traits of interest for
use in calculating expected progeny difference values to guide future selection
decisions.
Some examples of synthetic or composite
Composite Component breeds
Aust. Milking Zebu Sahiwal, Red Sindhi
Brangus Brahman, Angus
Murray Grey Roan Shorthorn, Angus
Santa Gertrudis Brahman, Shorthorn others

Polwarth Merino, Lincoln

Warridale Border Leicester, Merino
Corriedale Merino, Lincoln
Dorper Dorset, Persian fat-tail.

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MARKER ASSISTED SELECTION

The idea behind marker assisted selection is that there may be genes with
significant effects that may be targeted specifically in selection. Some traits are
controlled by single genes (e.g. hair colour) but most traits of economic importance
are quantitative traits that most likely are controlled by a fairly large number of
genes. However, some of these genes might have a larger effect. Such genes can be
called major genes located at QTLs. Although the term QTL strictly applies to genes
of any effect, in practice it refers only to major genes, as only those will be large
enough to be detected and mapped. Following the inheritance at such QTL might
assist in selection.
When making selection decisions based on marker genotypes, it is important to
know what information is exactly contained by the marker genotype. The figure
below shows the principle of inheritance of a marker and a linked QTL. We can
identify the marker genotype (Mm) but not the QTL (Gg). The last is really what we
want to know because of its effect on economically important traits.

Let the G allele have a positive effect, therefore being the preferred allele. In the
example, the M marker allele is linked to the G in the sire. Progeny that receive the
M allele from the sire, have a high chance of having also received the G allele, and
are therefore the preferred candidates in selection.
As shown in the figure above, there are 4 types of progeny. All progeny will inherit
m alleles and g alleles from the mother. The sire will provide them with either an M-
or an m- allele and either G or g. In the figure, 90% of the progeny that receives an
M-allele have also received a G-allele, because M and G alleles are linked on the
same chromosome in the sire. However, in 10% of the cases while the sire produces
gametes, there will be a recombination between the two loci, and animals that
inherited an M-allele from the father have received a g-allele rather than a Q-allele.
Marker alleles therefore do not always provide certainty out the genotype at the
relevant QTL.
Animals may be selected based on the marker information only. This is a good idea
only if the marker is linked to a single gene causing all of the genetic variation.
Usually we imagine that there may be a major gene/QTL, but there are many other

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important genes, not covered by the marker. In that case we want to combine the
information on markers with information on phenotype. The first aims to get the
good QTL, the second aims at getting also good 'other genes'. Selection with the aid
of information at genetic markers is termed marker assisted selection (MAS).

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NATIONAL AND INTERNATIONAL BREEDING PROGRAMMES

Stakeholders and their potential roles

The number, type and roles of stakeholders vary depending on the stage of
development of the breeding programme. In countries with well-developed and
functioning breeding programmes, there may be many stakeholders each with well-
defined roles. A very important aspect in starting up a breeding programme is
identifying and defining roles of key players and how they interact. This is because
activities related to the breeding programme have to take place at different
locations but are interdependent. This requires interaction and communication
between stakeholders. The figure below shows the classification of the key
stakeholders into four broad groups.
As can be seen in this figure, some subgroups are represented in more than one
group. The nucleus herds can be owned by breed societies, farmer co-operatives or
research stations owned by National Agricultural Research Systems (NARS). NARS
as used here include all public and parastatal organisations and private non-profit
institutions, such as universities, that conduct research or work on the
development or adaptation of technology, and policies that support agricultural and
rural development. If a breed society owns the nucleus herd, then its overall role in
the breeding programme would be the traditional role of the nucleus plus its role as
a collaborator. Similarly, the overall roles of NARS would be equivalent to the
summation of its roles as a collaborator, and policy and planning developer.

Stakeholders

Nucleus herd Participating herds Collaborators

Policy and planning
developers

Breed Societies Farmers NARS Government

Ministries

Cooperatives Farmers’ Training NARS

Centres

NARS Extension agents Farmers

Government Breed Societies

Ministries

Cooperatives

Consumers

Potential key stakeholders in a breeding programme for the Kenyan zebu breeds.
NARS = National Agricultural Research Systems e.g. universities, KARI and its
research centres, research stations etc.

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Nucleus herd

The nucleus’ main objective is the improvement of the indigenous cattle. The
nucleus should not be engaged in research per se, but should put in place
collaborative mechanisms with appropriate stakeholders (e.g. NARS) to ensure that
data coming out of the recording system are analysed, interpreted and the results
used. Such partnerships will also facilitate identification of emerging researchable
issues, which can support the effectiveness of the programme. The nucleus should
also be involved in activities directly related to the cattle owners e.g. extension
advice, open days, demonstrations and in identifying with the local community.
Performance and pedigree recording, and selection should be the major
preoccupation of the nucleus. The nucleus should, through its extension agents
and scientists, screen all the cows joining it from the participating herds. Nucleus
herds should provide superior genes to the participating herds and encourage
farmers to purchase their products through advertisements (e.g. marketing of the
breeding stock).

Participating herds (farmers)

Farmers own the animals and have responsibility for day-to-day decisions
concerning the animals in their herds, i.e. feeding, health management etc. In an
open nucleus system, this group may have the role of providing information on
their best animals so that these animals can be introduced into the nucleus. These
farmers are clients as well as proprietors of the breeding programme.

Collaborators

This group is important because it has the potential to provide additional human
operational and management resources. During the establishment stages, it is this
group that could provide additional funding resources.
National Agricultural Research Systems (NARS). These will be required in the early
stages of development of a genetic improvement initiative (e.g. to contribute to
capacity building) and when this initiative is operational. Qualified personnel to
run such an initiative are likely to be found in these systems. Therefore, they are
expected to be in charge of the technical support required in the implementation
and running of the genetic improvement programme. Their roles could include:
 Developing the breeding programme and estimation of genetic parameters
and economic values;
 Designing and evaluating the breeding programme (including quantifying
extent of genetic progress);
 Electronic data processing and genetic evaluation;
 Designing a system of mating and exchange of breeding animals between
locations/herds;
 Breeding advice;
 Training of staff to work in the breeding programme;
 Ensuring participation and co-operation through extension and education;
and

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  Research and development.
Farmers’ training centres and extension agents. Farmers’ training centres should
train the farmers in aspects of animal production and veterinary sciences, and not
just in genetic improvement. Farmers should be aware of what genetic change can
achieve and how this can occur. Training of farmers is the responsibility of
extension agents who should be able, with the help of researchers, to translate
research information into simple terms for the farmer to understand. The extension
agent is the contact person for the farmer in relation to improvement of animal
production. Experience has shown that the level of management in a farm is
positively correlated with the number of times a farmer is in contact with his/her
extension agent.
Breed societies. In a developed breeding programme, most of the roles of NARS
outlined above are performed by breed societies. In such systems, the nucleus is
normally made up of several herds located in different places and breed societies
generally co-ordinate the breeding activities between locations. Their other roles,
which could also be applicable in the Kenyan situation, include:

 Registration of animals based on set breed standards;

 Maintaining national records for registered animals as deemed necessary, in the
best interests of the breeders;
 Designing programmes to promote and protect the purity of the breed
consistent with the set standards;
 Promoting interest, spreading knowledge and supporting responsible breeding;
 Educating members and the general public (e.g. through demonstrations) about
proper care, maintenance and treatment of the breed; and
 Funding research work aimed at improving, protecting, preserving and
promoting the breed.

Co-operatives. Co-operatives can be farmer organisations when they are 100%

owned and run by farmers. Low returns per unit of production, inaccessibility of
resources and markets, and ignorance or lack of awareness are some of the
problems farmers face. Through co-operatives, farmers create their own
infrastructure of marketing and production support services e.g. services in animal
breeding, health care, feed, other inputs and credit facilities.

Consumers. Consumers drive the breeding programme in that they encourage the
breeders and producers to focus the programme in ways that reflect the market
(consumer) demand. Consumers also influence the breeding traits through their
preferences and purchasing power. For example, if consumers prefer meat with less
fat and are ready to pay high prices for lean meat, the breeding programmes must
be adjusted accordingly to reflect these preferences.

Policy and planning developers

This group should create an enabling environment in responding to consumer and

farmer needs. The government, through some of its departments, and with the help
of NARS would have to act as a regulatory body by developing animal breeding

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policies that ensure that the breeding programme is consistent with the overall
national goals. Such laws would regulate the activities of each stakeholder by
clearly outlining the roles of each. The policies would have to cover a wide area.
This group should also provide the broad policy decisions required in planning,
implementing and maintaining the operation. It may be important to effectively
involve farmers or farmer organisations in making these decisions. While farmers
will not have the capacity to understand the technical aspects of the programme,
they must be able to obtain practical interpretations associated with certain
decision options.

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ADVANCES AND APPLICATION OF BIOTECHNOLOGY

1. Transgenesis (gene transfer)

This means the stable incorporation of a gene from another species in such a way
that it functions in the receiving species and is passed on from one generation to
the next.
A variety of techniques are used in transgenesis, but the most common is the direct
injection of foreign DNA into the nucleus during the early embryonic stages.
- The success rate is often very low 2%.
- Most work has been carried out in mice, pigs and sheep.
One consequence of transgenesis is a large number of unacceptable side effects
may occur (e.g. in plants, introducing bacterial cells into plants – problem with
antibiotics in livestock).
 In dairy cattle, genes likely to modify fat or protein synthesis in the
mammary glands are being sought.
 Transfer of growth hormone from pigs to sheep has been carried out (aim to
obtain greater amounts of wool!)
 A cold tolerance gene has been transferred from flounder (fish) into Salmon.

Problems with transgenesis

 Genes transferred from one organism to another may react unpredictably

hence may be activated in unintended tissues and cause genetic defects.
 Transgene transmission to the next generation is also sometimes abnormal.
 Consumer apathy to transegnesis is very strong in many countries.
 Genes promoting productivity (meat, milk, wool) or reducing costs are most
likely to be found already in the species concerned!
 Costs of transgenesis is very high.
Technology for producing pharmaceuticals in milk now exist – with more than 25
separate transferred genes being expressed in the mammary glands and mammals
– mainly mice, but also in sheep, pigs, goats and rabbits.

2. Recombinant DNA technology (also called genetic engineering)

Definition: Can be defined as a collection of experimental techniques that enable

scientists to identify, isolate and propagate fragments of the genetic material (DNA
in pure form). It is in many ways an extension of natural processes, of adaptation
and evolution. Population of organisms are able to adapt to changes in the
environment because they are heterogeneous because of the acquisition of new
genes or the alteration of the existing genes. These natural processes of genetic
restructuring form the essence rDNA technology with an important difference being
that rDNA results are more made in the lab while those of the natural processes
are due to pressure of natural selection in the environment.

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Practical application of genetic engineering or rDNA technology

1. The commercial production of largely in accessible proteins. Several

mammalian proteins have now been obtained in large amounts in pure form
for the 1st time. These include insulin (human), HGH (human growth
hormone), bovine growth hormone or interferon (the experimental anti-viral,
anti-cancer drug which was previously available in quantities too small for
extensive clinical study) all of which can now be produced in large scale.
2. The production of vaccines has been very promising (e.g. Hepatitis B,
Influenza, Foot and mouth disease, etc.)
3. Preparation of blood coagulation factors and other hormones are in
development. Tissue Plasminogen Activator for dissolving blood clots is ready
for clinical study.
4. An understanding of genetic defects, in hereditary diseases and the
development of cure for such disease, for example by compensating for
missing enzymes.
5. A diminished dependency on petroleum is being sought through the
transformation of waste products from Agriculture, for the production of the
protein, fuel ethanol, and organic compounds such as glycerol, ethylene
oxide and ethylene glycol.
6. Mass production of highly in accessible enzymes. Some examples include the
production of urokinase to combat blood coagulation in stroke therapy.
7. Monoclonal antibodies – unique proteins that recognize and attach to
specific foreign substances in the body that can be used for diagnosis of
certain diseases and possible for treatment.
8. In the cultivation of plants, a number of research projects now underway
may lead to:
 Crops with increased resistance to salty soil, heat, cold, disease,
drought and other environmental conditions.
 Plants that produce their own insecticides or herbicides.
 Genetically engineered microbes that will protect plants from freezing
or that produce natural insecticides; or
 Food plants with enhanced nutritional value
9. The production of specific biodegrading organisms for pollution control.
10. Improvement of fermentation processes through the incorporation of
catabolic enzymes in the production strain, which thus gains the ability to
break down low-cost-substrate.
11. Production of bacterial enzymes. DNA ligase is useful in DNA recombinant
research and it is nowadays commercially produced by inserting copies of
DNA ligase gene into E.coli.

3. Artificial Insemination (AI)

Definition: A reproductive technology, in which semen is collected from males,

then used either in a fresh or frozen form to inseminate / breed females.
AI has been a practical technology for over 50 years.

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 - It is widely used in poultry, swine and cattle and to a lesser extent in
sheep and horses.
- To practice AI, the females must be detected in heat, moved to a breeding
area and properly inseminated at the right time.
AI has made possible widespread progeny testing of sires, and has facilitated the
use of bulls, in diverse environments.

Benefits of AI

1) One sire can have literally thousands of offspring

2) Frozen semen in some species e.g. cattle, can be stored almost indefinitely
3) AI increases selection intensity by providing access to the very best sires
from the very best herds / flocks.
4) In some species the relatively low cost of AI resulted in a marked increase in
the rate of genetic change.
5) AI makes it easier and less expensive to import new breeds.
6) Through AI, genes/germplasm can be saved for future use.
7) The spread of reproductive diseases can be greatly reduced using AI.
8) AI profoundly increases connectedness – the degree to which data from
different contemporary groups within a population can be compared as a
result of pedigree relationships between animals in different groups – thus
large scale genetic evaluation using BLUP procedures is possible e.g. work
carried out by INTERBULL for cattle.

4. Embryo Transfer (ET)

Def: Embryo Transfer is the process of removing embryos from a superior

cow and placing them in a surrogate cow where they develop into a calf. The
process is comparable to artificial insemination, which uses superior sperm to
reproduce quality bulls. By combining the two processes, a quality herd can be
developed in a short period of time.

Advantages of ET

1. As in AI, ET allows an animal to have more offspring than normal e.g. each
cow has upto 75,000 potential eggs (oocytes) in her ovaries. An average cow
will give birth to 4 calves in her lifetime. With ET, this could increase to 25
or more calves from a single cow.
2. ET provides breeders access to select / specific individuals. Genetically
superior dams (cows) can contribute more to a breeding programme when
ET is used.
3. ET increases the accuracy of selection and selection intensity for females
because they usually have relatively few offspring.
4. ET is the safest way to import / export germplasm because embryo’s are less
likely yo harbor disease organisms that either frozen semen or live animals.
5. Because embryos are whole individuals, freezing and storing them preserves
both genes and gene combinations.

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 6. In developing countries MOET schemes could be used for rapid expansion of
rare genetic stock.

Disadvantages of ET

1. High cost. An ET calf costs between 500 and 1000 US$ more to produce
than a calf born of conventional reproduction.
2. Pregnancy rates can be low – 60% with unfrozen embryos and may fall to
10% with frozen embryos.

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