In chapter 7 simple methods for calculation of the estimated breeding values
were given. Only data with uniform relation were included. This is a very simple form of
estimation of the breeding value and it demands very little computer power,
as it is based on the calculation of a single weight factor. This weight factor
is dependent on the heritability, the number of individuals in the group, their internal
relations and how they are related to the candidate. With the introduction of modern
computers, the basis for a better utilization of existing data for estimation of
the breeding value has materialized. The more
advanced methods are based on systems of linear equation, in which every observation
is provided with its own equation.
Thereby the creation of a system of equations for calculation of the weight factors for every
observation is completed. The model includes all observations from related animal
as well as observations from related to related.
An example of related to related animals: The mother of an offspring, whose father
is being evaluated. If the mother of the offspring
is present in the model of the father's estimated breeding value, the
preconditions for random mating does not have to be too strict, since the father's breeding value
has been adjusted for deviations of the dam's from the population mean value.
The implied condition, that all observations must occur in the same environment, can also be
taken more lightly, since for
instance herd average can be taken into account in the models. To do this the model implies that more than one
family per herd must be represented and that some of the sires in the herd should
be used in other herds as well.
The most important methods for estimation of breeding value:
1. Selection index (SI)
2. Best linear prediction (BLP)
3. Best linear unbiased prediction (BLUP)
4. Animal model (AM)
5. Genomic selection
The selection index was developed in the 1950's and was utilized before the computer age. The SI is at
present mainly used for model calculation, as it is useful for the evaluation of the effect of multi trait
selection. If a certain set of economic weight factors are used, each trait will
get an expected delta G's. Before the computer
age it was common to pre-correct data, as for instance for calving age or slaughter weight,
since these traits had some biological variation. Pre-correction is still applied to some extent.
With the production of faster computers, it has been possible to develop models, which
calculate
estimated breeding value for all animals in a population. When this is the case, it is
easier to use all information from all the individuals, as these will give information
on relations to all the others. BLP does not include
environmental factors, which makes is less useful. The solutions obtained from a few animals are identical to
the solutions
obtained by SI.
The BLUP and AM can simultaneous estimate environmental effects and
the correction for them.
In the last three methods the relationship matrix is utilized at varying degrees.
The
relationship matrix is arranged according to the tabular method given in
section 4.4. The solution of that many equations cannot be
done explicitly, which have been taught when to solve two equations with two unknowns.
The solutions are
first based on guesses and then on recalculation until the solutions
remain constant. This method is called iterative. It is possible, by means of this method, to estimate breeding values
of millions of animals simultaneously. At present the AM method is used in the
both Danish dairy and swine breeding work.
Genomic selection by Thomas Mark
Genomic selection is a new technology in which breeding values are predicted from genome-wide markers
in the form of single nucleotide polymorphisms SNP. The genetic maps are based on SNP and they enable us to
divide the entire genome into thousands of relatively small chromosome segments. Then the effects of each
chromosome segment are estimated simultaneously. Finally, the genomic breeding value equals the sum of all
estimated chromosome segment effects. The chromosome segment effects can be estimated for a group of animals
(ie a reference population); and for any remaining animal, only a blood or tissue sample is needed to determine
its genomic breeding value. The chromosome segment effects apply to all animals in the population in which they
were estimated, because markers are in linkage disequilibrium with the causal gene that they bracket.
Genome-wide information allows accurate selection of young animals provided that phenotypes from sufficiently many
reference animals are available. This means that genomic breeding values are especially beneficial when
traditional selection is difficult such as when phenotypic recording is restricted by sex and age (e.g.
very beneficial for dairy cattle). However, conservative use of young animals without phenotypic
information (relating to self or progeny) is advised to avoid potential negative side effects related
to unfavourable mutations, unfavourable selection pressure on non-recorded breeding goal traits and
high rates of inbreeding.