genetic markers in characterization2

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THE USE OF GENETIC MARKERS IN CHARACTERIZATION OF

LIVESTOCK BREEDS

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• FAO estimates that there more than 6,300 breeds of livestock in the world belonging to 30 domesticated species.

• These breeds were developed following domestication and natural and human selection over the past 12,000 years.

• Different livestock populations have evolved unique characteristics for adaptation to their production systems and agro-ecological environments.

• Their genetic diversity has provided the material for the very successful breeding programmes in the 19 and 20th century.

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• These livestock breeds represent a unique resource to respond to the present and future needs of livestock production, both in developed and developing countries.

• However, livestock diversity is shrinking rapidly.

• Loss of biological diversity is a serious problem or potential problem in many places of the world.

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• FAO estimates that 12% of documented breeds are extinct and at least 30% of the remaining breeds are at risk of loss i.e. with less than 1000 breeding female or less than 20 breeding males .

• As the importance of a particular breed for the future could not be determined from current value of the animal, efforts should be made to preserve every unique breed.

• Resources, both in terms of finances and manpower, are limited while large number of indigenous breeds are in endanger of extinction. Hence, all breeds cannot be given the same priority for conservation.

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• One of the most important problems in the conservation of domestic animal resources is how to choose appropriate breeds or strains for conservation, among many available now, such that maximum genetic diversity could be maintained.

• Characterisation of livestock breeds has been based on description of morphological characters such as horns, ears, coat colour, body size and production and reproductive traits.

• Since livestock breeds in developing countries have not been subjected to selection for specific traits, considerable phenotypic variation is observed within and among populations with regard to size, horn and ear types and coat colour. Furthermore, most productive traits are polygenetically inherited and are influenced by environment effects, sometimes with a genotype x environment interaction.

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• This leads to some inconsistencies in the classification of the various populations into breeds or strains.

• Therefore, reliance on phenotypic characters as the basis for characterisation of breeds for sustainable utilization and conservation may be misleading.

• Molecular genetic characterisation is factual and precise. It is in this sphere that molecular biotechnology has an important role to play.

• Genetic characterisation of livestock species involves estimation of the genetic uniqueness of the breeds or strains and their evolutionary relationships. This can provide information on which of the populations represent homogenous breeds or strains and which are different. Such knowledge will enable decision-making regarding the choice of breeds or strains for conservation.

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Measurement of genetic variation

• Genetic variation can be measured at various levels (i.e. morphological, protein and DNA),

• Molecular genetic characterization explores polymorphism in selected protein molecules and DNA markers in order to measure genetic variation at the population level.

• Protein polymorphisms were the first molecular markers used in genetic characterisation of livestock breeds (1960s - 1970s).

• This involved the characterisation of blood group and allozyme systems (allelic variants of enzymes encoded by structural genes ) of livestock.

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• However, the level of polymorphisms observed in proteins is low, this has reduced the applicability of protein typing in genetic diversity studies.

• Furthermore, allozymes are phenotypic markers, hence, they may be affected by environmental conditions.

• DNA-level polymorphisms are the markers of choice for molecular genetic characterization.

• Because they are based on differences in the DNA sequence, DNA markers are not environmentally influenced, which means that the same banding profiles can be expected at all times for the same genotype.

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• A variety of DNA markers are used for the study of genetic variation at the DNA level. The most widely used are:

- Restriction fragment length polymorphisms (RFLPs),

- Minisatellite,- Microsatellite, - Random amplified polymorphic DNA (RAPD),- Amplified fragment length polymorphisms

(AFLPs) and,- Single nucleotide polymorphisms (SNPs).

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• These markers provide the means to examine directly nucleotide sequence differences in the DNA and to determine the amount of genetic variation present in the populations.

• Variation at DNA level is identified by gel electrophoresis.

• Among the DNA markers, microsatellites are very useful for the study of genetic variation within and between closely related populations such as livestock breeds or strains.

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Measuring genetic diversity within a population

• Within population diversity is measured by allelic diversity, which is the average number of alleles at a locus across all loci analysed.

• Another measure of within population genetic diversity is observed heterozygosity, which is the total number of heterozygotes divided by the total number of animals that have been analysed.

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• A more useful measure of within population genetic diversity is expected heterozygosity or gene diversity, calculated from estimated allele frequencies. Expected heterozygosity (He) = 1 – Σpi

2, where pi is the frequency of the ith allele at the locus. Expected heterozygosity can be calculated as average across all loci analysed to get average heterozygosity (H). H is the average proportion of heterozygotes per locus in a randomly mating population. It is also equal to the expected proportion of heterozygous loci in a randomly chosen individual.

• Another parameter of within population diversity is the proportion of polymorphic loci, which is the proportion of loci at which there is more than one allele.

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Microsatellite gel image

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Genetic structure of populations

• To study the genetic structure of a population we must describe the gene pool of the group quantitatively.

• This is done by calculating genotypic frequencies and allelic frequencies within the population.

• Genotypic frequencies – the proportions or percentages of different genotypes found within a population.

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• To calculate genotypic frequencies at a specific locus, we count the number of individuals with one particular genotype and divide this number by the total number of individuals in the population.

• Allelic frequencies – the frequencies of alleles at a locus occurring among individuals in a population.

• Allele frequencies can be calculated in either of two ways: from the observed numbers of different genotypes at a particular locus, or from the genotypic frequencies.

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• Calculating the frequencies directly from the numbers of genotypes:

- allelic frequency = numbers of copies of a given allele in the population/sum of alleles in the population.

- In diploid individuals allelic frequency = (2x number of homozygotes) + (number of heterozygotes)/(2 x total number of individuals)

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• Calculating allelic frequencies from genotypic frequencies.

• The frequencies of two alleles, f(A1) and f (A2) are symbolized as p and q.

• p = f(A1) = (frequency of the A1A1 homozygote) + (½x frequency of the A1 A2 heterozygote)

• q = f(A2) = (frequency of the A2A2 homozygote) + (½x frequency of the A1 A2 heterozygote)

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The Hardy-Weinberg Law

• In the Hardy-Weinberg model, the mathematical relation between allele frequencies and the genotype frequencies is given by : A1A1: p2; A1A2 : 2pq; A2A2 : q2

- where p2, 2pq and q2 are the frequencies of the genotypes A1A1, A1A2 and A2A2, respectively, in any generation.

- p and q are the allele frequencies of A1 and A2 alleles, respectively, and p + q = 1.

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• The Hardy-Weinberg law states that, in an infinitely large, randomly mating population in which there is no mutation, migration and natural selection, the frequencies of the alleles do not change over time, and as long as mating is random, the genotypic frequencies will remain in the proportions p2, 2pq and q2 , where p is the allelic frequency of A1 and q is the allelic frequency of A2. The sum of the genotypic frequencies should be equal to 1, that is p2 + 2pq + q2 = 1.

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Average expected heterozygosity (He) (gene diversity) - It is the probability that, at a single locus, any two alleles, chosen at random from the population, are different to each other.

Three calculations are possible:

• A locus with two alleles: hj = 1 – p2 – q2

• A locus j with i alleles: hj = 1 – Σpi2

• Average for several loci: H = ΣjLhj/L

The average He over all loci is an estimate of the extent of genetic variability in the population.

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Calculating within population diversity with a codominant molecular marker

Locus A

Locus B

Locus E

Locus C

Locus D

M 1 2 3 4 5 6 7 8 9 10

Gel11 12 13 14 15 16 17 18 19 21 22 23 24 25 26 27 28 2920 30

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Scoring of genotypes for locus A

Indi. 1 2 3 4 5 6 7 8 9 10

Gen. A1A2 A2A2 A1A2 A2A2 A2A2 A2A2 A2A2 A2A2 A2A2 A1A1

Indi. 11 12 13 14 15 16 17 18 19 20


Indi. 21 22 23 24 25 26 27 28 29 30


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Locus Data analysisAllelefreq.

hj = (1 - p2 - q2)

Hi

A

Genotypes A1 A1 A1 A2 A2 A2 Total

p qGen. freq. (exp.) p2 2pq q2 1

Individuals (no.) 2 4 24 30

Gen. freq. (obs.) P11 = 0.07 P12 = 0.13 P22 = 0.80 1 0.13 0.87 0.23

B

Genotypes B1 B1 B1 B2 B2 B2 Total



Gen. freq. (obs.) P11 = 0.23 P12 = 0.10 P22 = 0.67 1 0.28 0.72 0.41

E

Genotypes E1 E1 E1 E2 E2 E2 Total



Gen. freq. (obs.) P11 = 0.50 P12 = 0.27 P22 = 0.23 1 0.63 0.37 0.46 0.22

Calculating within population genetic diversity

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Measuring genetic diversity between populations

• Genetic variation between populations (species, breeds or strains) is measured by assessing the genetic uniqueness of the breeds.

• To know the uniqueness of a breed or strain, one must study the genetic variation in a set of breeds.

• Genetic uniqueness of breeds or strains is measured by the relative genetic distances of such breeds or strains from each other.

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• Genetic distance is the extent of gene differences (genomic difference) between breeds or species that is measured by some numerical quantity. The genetic distance between two populations is defined in terms of differences in allele frequencies for all loci analysed.

• There are many different measures of genetic distances. The most commonly used is the Nei’s standard genetic distance (DS).

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• The basis of this genetic distance is the normalized identity, which is the probability that a randomly chosen allele from each of two different populations will be identical.

• A large number of loci have to be examined, in livestock breeds a recommended number of loci is 15 to 20.

• The number of individuals per breed or strain should be not less than 40 animals (include both males and females in about equal proportions).

• Genetic distance is estimated for all pair-wise combinations of a set of populations (species or breeds or strains).

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Example of calculating standard genetic distance

- Extract DNA from all individuals of each population.

- Perform PCR or Southern blotting (depending on the type of DNA marker).

- Determine the genotypes of the markers at different loci by gel electrophoresis and visualization of the bands.

- Calculate allele frequencies for all loci in each population. Allele frequencies = the number of times an allele is observed in a population divided by the total number of observation or 2n, with n being the number of individuals analysed in the population.

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• Calculate genetic distances between pairs of populations (see the example below).

Alleles Allele frequencies in population X

Allele frequencies in population Y

1 p1 = 0.60 q1 = 0.30

2 p2 = 0.30 q2 = 0.60

3 p3 = 0.05 q3 = 0

4 p4 = 0.05 q4 = 0

5 p5 = 0 q5 = 0.10

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• Standard genetic distance (DS) = -ln(I), I is the normalized identity.

• I = JXY/√ JXX.JYY

- Where JXX = Σpi2 = 0.602 + 0.302 + …+ 02 =

0.455.

- Where JYY = Σqi2 = 0.302 + 0.602 + …+ 0.102

= 0.460.

- Where JXY = Σpi qi= 0.60 x 0.30 + 0.30 x

0.60 …+ 0 x 0.10 = 0.360.

I = 0.360/√(0.455 x 0.460) = 0.7869

Ds = -ln(0.7869) = 0.2397

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• The genetic distance data are then used to construct a phylogenetic tree. The phylogenetic tree shows the relationship of each population to every other population.

• The purpose of phylogenetic studies are:-

(i) To reconstruct the correct genealogical relationships between populations

(ii) To estimate the time of divergence between populations since they last shared a common ancestor.

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• A phylogenetic tree is a graph composed of nodes and branches, in which only one branch connects any two adjacent nodes.

• The nodes represent the taxonomic units, which can be species, breeds, strains, populations or genes.

• The branches define the relationships among the taxonomic units in terms of descent and ancestry.

• The branching pattern of a tree is called the topology.

• The branch length represents the number of changes that have occurred in that branch.

• The taxonomic units represented by the nodes can be species, breeds, populations, individuals, or genes.

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• There are two types of nodes, external (or terminal) nodes and internal nodes. For example in figure 1 nodes A,B,C, D and E are external, whereas F, G and H are internal.

• External nodes represent the extant taxonomic units under comparison and are called operational taxonomic units (OTUs). Internal nodes represent ancestral units.

• The branches in a phylogenetic tree can be unscaled (fig. 1 a); their length are not proportional to the number of changes, which are indicated on the branches.

• In the unscaled phylogenetic tree, the extant OTUs are lined up and the nodes are placed on a time scale representing the divergence events.

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• In the scaled phylogenetic tree (fig 1b), the length of the branches are proportional to the numbers of changes.

• Phylogenetic trees can be either rooted or unrooted. In a rooted tree there is a particular node, called the root (R in figure 2a), which is the common ancestor of all the OTUs under study. A unique path from the root leads to any other node. The direction of each path corresponds to the evolutionary time.

• An unrooted tree (fig 2 b) is a tree that only specifies the relationships among the OTUs but does not define the evolutionary path. Unrooted trees do not make assumptions or require knowledge about common ancestor.

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Fig 1: Phylogenetic tree for five OTUs(a) Unscaled branches (b) Scaled branches

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Fig 2: (a) Rooted phylogenetic tree(b) Unrooted phylogenetic tree

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• There are two ways of building trees: clustering methods and exhaustive search methods.

• Clustering methods use specific algorithm for constructing the best tree. Examples : the unweighted pair group method with arithmetic averages (UPGMA) and Neighbour-Joining method (NJ).

• They are easy to perform, resulting in very fast computer programmes.

• Their limitation is that they do not provide a ranking criterion for evaluating trees

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• Exhaustive search methods use optimality criteria for choosing the best tree among the set of all possible trees, e.g. maximum parsimony and maximum likelihood methods.

• The criterion is used to assign to each tree a score or a rank, which is a function of the relationship between the tree and data.

• They have the advantage that they provide a ranking criterion under which all trees can be evaluated.

• They are computationally demanding, resulting in computer programmes which are much slower to perform.

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• In the phylogenetic tree, the length of the branches separating any two populations is directly proportional to the extent to which those population differ in terms of allele frequencies.

• For conservation purpose it is recommended to choose breeds that show the widest genetic differences between them.

• This ensures that the breeds chosen possess different alleles and gene combinations and thus have sufficient genetic variation to adapt to the environmental changes.

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The unweighted pair-group method with arithmetic mean (UPGMA)

• The UPGMA, the simplest method of tree reconstruction, was originally developed for constructing taxonomic trees that reflect the phenotypic similarities between OTUs.

• It can also be used to construct phylogenetic trees if the rates of evolution are approximately constant among the different lineages.

• The UPGMA method employs a sequential clustering algorithm, in which local topological relationships are inferred in order of decreasing similarity and the tree is built in a stepwise manner.

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• First identify the two OTUs that are most similar to each other (i.e. have the shortest distance) and treat them as a new single OUT.

• From among the new group of OTUs identify the pair with the highest similarity, and so on, until only two OTUS are left.

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Fig 3: Stepwise construction of a phylogenetic tree using UPGMA

methods

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• d(AB)C = (dAC + dBC)/2

• d(AB)D = (dAD + dBD)/2

• D(ABC)D = ((dAD + dBD + dCD)/3)/2

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Genetic distances between hominoid species

Human Chimpanzee Gorilla Orang-utan

Gibbon

Human - 0.0919 0.1083 0.1790 0.2057

Chimpanzee - 0.1134 0.1940 0.2168

Gorilla - 0.1882 0.2170

Orang-utan - 0.2172

Gibbon

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Fig 4: Phylogenetic trees of four main groups of cattle derived from microsatellite analysis (left) and mitochondrial DNA analysis (right)

European Bos taurus African Bos taurus African Bos indicus Indian Bos indicus Microsatellite diversity Mitochondiral diversity

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Example of a phylogenetic tree showing genetic relationships among goat populations